idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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801 | Can a probability distribution value exceeding 1 be OK? | When random variable $X$ is continuous and its probability density function is $f(x)$, $f(x)dx$ is a probability, but $f(x)$ is not a probability and can be larger than one.
The reported $f(\mbox{height}|\mbox{male})$ is not a probability, but $f(\mbox{height}|\mbox{male})d\mbox{height}$ is.
In other words, for a cont... | Can a probability distribution value exceeding 1 be OK? | When random variable $X$ is continuous and its probability density function is $f(x)$, $f(x)dx$ is a probability, but $f(x)$ is not a probability and can be larger than one.
The reported $f(\mbox{hei | Can a probability distribution value exceeding 1 be OK?
When random variable $X$ is continuous and its probability density function is $f(x)$, $f(x)dx$ is a probability, but $f(x)$ is not a probability and can be larger than one.
The reported $f(\mbox{height}|\mbox{male})$ is not a probability, but $f(\mbox{height}|\m... | Can a probability distribution value exceeding 1 be OK?
When random variable $X$ is continuous and its probability density function is $f(x)$, $f(x)dx$ is a probability, but $f(x)$ is not a probability and can be larger than one.
The reported $f(\mbox{hei |
802 | Can a probability distribution value exceeding 1 be OK? | The point value at a particular parameter value of a probability density plot would be a likelihood, right? If so, then the statement might be corrected by simply changing P(height|male) to L(height|male). | Can a probability distribution value exceeding 1 be OK? | The point value at a particular parameter value of a probability density plot would be a likelihood, right? If so, then the statement might be corrected by simply changing P(height|male) to L(height|m | Can a probability distribution value exceeding 1 be OK?
The point value at a particular parameter value of a probability density plot would be a likelihood, right? If so, then the statement might be corrected by simply changing P(height|male) to L(height|male). | Can a probability distribution value exceeding 1 be OK?
The point value at a particular parameter value of a probability density plot would be a likelihood, right? If so, then the statement might be corrected by simply changing P(height|male) to L(height|m |
803 | Choice of K in K-fold cross-validation | The choice of $k = 10$ is somewhat arbitrary. Here's how I decide $k$:
first of all, in order to lower the variance of the CV result, you can and should repeat/iterate the CV with new random splits. This makes the argument of high $k$ => more computation time largely irrelevant, as you anyways want to calculate many ... | Choice of K in K-fold cross-validation | The choice of $k = 10$ is somewhat arbitrary. Here's how I decide $k$:
first of all, in order to lower the variance of the CV result, you can and should repeat/iterate the CV with new random splits. | Choice of K in K-fold cross-validation
The choice of $k = 10$ is somewhat arbitrary. Here's how I decide $k$:
first of all, in order to lower the variance of the CV result, you can and should repeat/iterate the CV with new random splits. This makes the argument of high $k$ => more computation time largely irrelevant,... | Choice of K in K-fold cross-validation
The choice of $k = 10$ is somewhat arbitrary. Here's how I decide $k$:
first of all, in order to lower the variance of the CV result, you can and should repeat/iterate the CV with new random splits. |
804 | Choice of K in K-fold cross-validation | Larger K means less bias towards overestimating the true expected error (as training folds will be closer to the total dataset) but higher variance and higher running time (as you are getting closer to the limit case: Leave-One-Out CV).
If the slope of the learning curve is flat enough at training_size = 90% of total d... | Choice of K in K-fold cross-validation | Larger K means less bias towards overestimating the true expected error (as training folds will be closer to the total dataset) but higher variance and higher running time (as you are getting closer t | Choice of K in K-fold cross-validation
Larger K means less bias towards overestimating the true expected error (as training folds will be closer to the total dataset) but higher variance and higher running time (as you are getting closer to the limit case: Leave-One-Out CV).
If the slope of the learning curve is flat e... | Choice of K in K-fold cross-validation
Larger K means less bias towards overestimating the true expected error (as training folds will be closer to the total dataset) but higher variance and higher running time (as you are getting closer t |
805 | Choice of K in K-fold cross-validation | I don't know how K affects accuracy and generalization, and this may depend on the learning algorithm, but it definitely affects the computational complexity almost linearly (asymptotically, linearly) for training algorithms with algorithmic complexity linear in the number of training instances. The computational time ... | Choice of K in K-fold cross-validation | I don't know how K affects accuracy and generalization, and this may depend on the learning algorithm, but it definitely affects the computational complexity almost linearly (asymptotically, linearly) | Choice of K in K-fold cross-validation
I don't know how K affects accuracy and generalization, and this may depend on the learning algorithm, but it definitely affects the computational complexity almost linearly (asymptotically, linearly) for training algorithms with algorithmic complexity linear in the number of trai... | Choice of K in K-fold cross-validation
I don't know how K affects accuracy and generalization, and this may depend on the learning algorithm, but it definitely affects the computational complexity almost linearly (asymptotically, linearly) |
806 | Choice of K in K-fold cross-validation | Solution:
K = N/N*0.30
N = Size of data set
K = Fold
Comment: We can also choose 20% instead of 30%, depending on size you want to choose as your test set.
Example: If data set size: N=1500; K=1500/1500*0.30 = 3.33; We can choose K value as 3 or 4
Note:
Large K value in leave one out cross-validation would result... | Choice of K in K-fold cross-validation | Solution:
K = N/N*0.30
N = Size of data set
K = Fold
Comment: We can also choose 20% instead of 30%, depending on size you want to choose as your test set.
Example: If data set size: N=1500; K=150 | Choice of K in K-fold cross-validation
Solution:
K = N/N*0.30
N = Size of data set
K = Fold
Comment: We can also choose 20% instead of 30%, depending on size you want to choose as your test set.
Example: If data set size: N=1500; K=1500/1500*0.30 = 3.33; We can choose K value as 3 or 4
Note:
Large K value in leav... | Choice of K in K-fold cross-validation
Solution:
K = N/N*0.30
N = Size of data set
K = Fold
Comment: We can also choose 20% instead of 30%, depending on size you want to choose as your test set.
Example: If data set size: N=1500; K=150 |
807 | A list of cost functions used in neural networks, alongside applications | Here are those I understand so far. Most of these work best when given values between 0 and 1.
Quadratic cost
Also known as mean squared error, this is defined as:
$$C_{MST}(W, B, S^r, E^r) = 0.5\sum\limits_j (a^L_j - E^r_j)^2$$
The gradient of this cost function with respect to the output of a neural network and some ... | A list of cost functions used in neural networks, alongside applications | Here are those I understand so far. Most of these work best when given values between 0 and 1.
Quadratic cost
Also known as mean squared error, this is defined as:
$$C_{MST}(W, B, S^r, E^r) = 0.5\sum\ | A list of cost functions used in neural networks, alongside applications
Here are those I understand so far. Most of these work best when given values between 0 and 1.
Quadratic cost
Also known as mean squared error, this is defined as:
$$C_{MST}(W, B, S^r, E^r) = 0.5\sum\limits_j (a^L_j - E^r_j)^2$$
The gradient of th... | A list of cost functions used in neural networks, alongside applications
Here are those I understand so far. Most of these work best when given values between 0 and 1.
Quadratic cost
Also known as mean squared error, this is defined as:
$$C_{MST}(W, B, S^r, E^r) = 0.5\sum\ |
808 | A list of cost functions used in neural networks, alongside applications | Don't have the reputation to comment, but there are sign errors in those last 3 gradients.
In the KL divergence, $$\eqalign{
C &= \sum_j E_j\log(E_j/a_j) \cr
&= \sum_j E_j\log(E_j) - E_j\log(a_j) \cr\cr
dC &= -\sum_j E_j\,\,d\log(a_j) \cr
&= -\sum_j (E_j/a_j)\,da_j \cr\cr
\nabla_a C &= \frac{-E}{a} \cr\cr
}$$
T... | A list of cost functions used in neural networks, alongside applications | Don't have the reputation to comment, but there are sign errors in those last 3 gradients.
In the KL divergence, $$\eqalign{
C &= \sum_j E_j\log(E_j/a_j) \cr
&= \sum_j E_j\log(E_j) - E_j\log(a_j) | A list of cost functions used in neural networks, alongside applications
Don't have the reputation to comment, but there are sign errors in those last 3 gradients.
In the KL divergence, $$\eqalign{
C &= \sum_j E_j\log(E_j/a_j) \cr
&= \sum_j E_j\log(E_j) - E_j\log(a_j) \cr\cr
dC &= -\sum_j E_j\,\,d\log(a_j) \cr
... | A list of cost functions used in neural networks, alongside applications
Don't have the reputation to comment, but there are sign errors in those last 3 gradients.
In the KL divergence, $$\eqalign{
C &= \sum_j E_j\log(E_j/a_j) \cr
&= \sum_j E_j\log(E_j) - E_j\log(a_j) |
809 | How to intuitively explain what a kernel is? | Kernel is a way of computing the dot product of two vectors $\mathbf x$ and $\mathbf y$ in some (possibly very high dimensional) feature space, which is why kernel functions are sometimes called "generalized dot product".
Suppose we have a mapping $\varphi \, : \, \mathbb R^n \to \mathbb R^m$ that brings our vectors i... | How to intuitively explain what a kernel is? | Kernel is a way of computing the dot product of two vectors $\mathbf x$ and $\mathbf y$ in some (possibly very high dimensional) feature space, which is why kernel functions are sometimes called "gene | How to intuitively explain what a kernel is?
Kernel is a way of computing the dot product of two vectors $\mathbf x$ and $\mathbf y$ in some (possibly very high dimensional) feature space, which is why kernel functions are sometimes called "generalized dot product".
Suppose we have a mapping $\varphi \, : \, \mathbb R... | How to intuitively explain what a kernel is?
Kernel is a way of computing the dot product of two vectors $\mathbf x$ and $\mathbf y$ in some (possibly very high dimensional) feature space, which is why kernel functions are sometimes called "gene |
810 | How to intuitively explain what a kernel is? | A visual example to help intuition
Consider the following dataset where the yellow and blue points are clearly not linearly separable in two dimensions.
If we could find a higher dimensional space in which these points were linearly separable, then we could do the following:
Map the original features to the higher, t... | How to intuitively explain what a kernel is? | A visual example to help intuition
Consider the following dataset where the yellow and blue points are clearly not linearly separable in two dimensions.
If we could find a higher dimensional space in | How to intuitively explain what a kernel is?
A visual example to help intuition
Consider the following dataset where the yellow and blue points are clearly not linearly separable in two dimensions.
If we could find a higher dimensional space in which these points were linearly separable, then we could do the following... | How to intuitively explain what a kernel is?
A visual example to help intuition
Consider the following dataset where the yellow and blue points are clearly not linearly separable in two dimensions.
If we could find a higher dimensional space in |
811 | How to intuitively explain what a kernel is? | A very simple and intuitive way of thinking about kernels (at least for SVMs) is a similarity function. Given two objects, the kernel outputs some similarity score. The objects can be anything starting from two integers, two real valued vectors, trees whatever provided that the kernel function knows how to compare them... | How to intuitively explain what a kernel is? | A very simple and intuitive way of thinking about kernels (at least for SVMs) is a similarity function. Given two objects, the kernel outputs some similarity score. The objects can be anything startin | How to intuitively explain what a kernel is?
A very simple and intuitive way of thinking about kernels (at least for SVMs) is a similarity function. Given two objects, the kernel outputs some similarity score. The objects can be anything starting from two integers, two real valued vectors, trees whatever provided that ... | How to intuitively explain what a kernel is?
A very simple and intuitive way of thinking about kernels (at least for SVMs) is a similarity function. Given two objects, the kernel outputs some similarity score. The objects can be anything startin |
812 | How to intuitively explain what a kernel is? | Very simply (but accurately) a kernel is a weighing factor between two sequences of data. This weighing factor can assign more weight to one "data point" at one "time point" than the other "data point", or assign equal weight or assign more weight to the other "data point" and so on.
This way the correlation (dot prod... | How to intuitively explain what a kernel is? | Very simply (but accurately) a kernel is a weighing factor between two sequences of data. This weighing factor can assign more weight to one "data point" at one "time point" than the other "data point | How to intuitively explain what a kernel is?
Very simply (but accurately) a kernel is a weighing factor between two sequences of data. This weighing factor can assign more weight to one "data point" at one "time point" than the other "data point", or assign equal weight or assign more weight to the other "data point" a... | How to intuitively explain what a kernel is?
Very simply (but accurately) a kernel is a weighing factor between two sequences of data. This weighing factor can assign more weight to one "data point" at one "time point" than the other "data point |
813 | How to intuitively explain what a kernel is? | In the top answer to this question, there's a link to the lecture of Pr. Yaser Abu-Mostafa from CalTech and it gives a very nice intuition of it... so I'll try to explain what i understood, without equation:
a kernel is a function (relatively simple to compute) taking two vectors (living in the X space) and returning ... | How to intuitively explain what a kernel is? | In the top answer to this question, there's a link to the lecture of Pr. Yaser Abu-Mostafa from CalTech and it gives a very nice intuition of it... so I'll try to explain what i understood, without eq | How to intuitively explain what a kernel is?
In the top answer to this question, there's a link to the lecture of Pr. Yaser Abu-Mostafa from CalTech and it gives a very nice intuition of it... so I'll try to explain what i understood, without equation:
a kernel is a function (relatively simple to compute) taking two v... | How to intuitively explain what a kernel is?
In the top answer to this question, there's a link to the lecture of Pr. Yaser Abu-Mostafa from CalTech and it gives a very nice intuition of it... so I'll try to explain what i understood, without eq |
814 | How to intuitively explain what a kernel is? | The kernel is a function that quantifies similarity between a pair of data points. And mathematically speaking this similarity can be computed using inner product, which has been explained beautifully in above answers.
In RBF kernel it calculates this similarity between a landmark point and all other data points. How t... | How to intuitively explain what a kernel is? | The kernel is a function that quantifies similarity between a pair of data points. And mathematically speaking this similarity can be computed using inner product, which has been explained beautifully | How to intuitively explain what a kernel is?
The kernel is a function that quantifies similarity between a pair of data points. And mathematically speaking this similarity can be computed using inner product, which has been explained beautifully in above answers.
In RBF kernel it calculates this similarity between a la... | How to intuitively explain what a kernel is?
The kernel is a function that quantifies similarity between a pair of data points. And mathematically speaking this similarity can be computed using inner product, which has been explained beautifully |
815 | How to intuitively explain what a kernel is? | as was said in comments @ttnphns: "Dot product and projection are not quite identical." –
kernel trick is utilized in computations involving the dot products (x, y).
kernel itself programmically is just a core of a computer's OS, that controls & distributes "time" & "power" between CPU, RAM, Devices & etc.
projection i... | How to intuitively explain what a kernel is? | as was said in comments @ttnphns: "Dot product and projection are not quite identical." –
kernel trick is utilized in computations involving the dot products (x, y).
kernel itself programmically is ju | How to intuitively explain what a kernel is?
as was said in comments @ttnphns: "Dot product and projection are not quite identical." –
kernel trick is utilized in computations involving the dot products (x, y).
kernel itself programmically is just a core of a computer's OS, that controls & distributes "time" & "power" ... | How to intuitively explain what a kernel is?
as was said in comments @ttnphns: "Dot product and projection are not quite identical." –
kernel trick is utilized in computations involving the dot products (x, y).
kernel itself programmically is ju |
816 | What's the difference between variance and standard deviation? | The standard deviation is the square root of the variance.
The standard deviation is expressed in the same units as the mean is, whereas the variance is expressed in squared units, but for looking at a distribution, you can use either just so long as you are clear about what you are using. For example, a Normal distrib... | What's the difference between variance and standard deviation? | The standard deviation is the square root of the variance.
The standard deviation is expressed in the same units as the mean is, whereas the variance is expressed in squared units, but for looking at | What's the difference between variance and standard deviation?
The standard deviation is the square root of the variance.
The standard deviation is expressed in the same units as the mean is, whereas the variance is expressed in squared units, but for looking at a distribution, you can use either just so long as you ar... | What's the difference between variance and standard deviation?
The standard deviation is the square root of the variance.
The standard deviation is expressed in the same units as the mean is, whereas the variance is expressed in squared units, but for looking at |
817 | What's the difference between variance and standard deviation? | You don't need both. They each have different purposes. The SD is usually more useful to describe the variability of the data while the variance is usually much more useful mathematically. For example, the sum of uncorrelated distributions (random variables) also has a variance that is the sum of the variances of th... | What's the difference between variance and standard deviation? | You don't need both. They each have different purposes. The SD is usually more useful to describe the variability of the data while the variance is usually much more useful mathematically. For exam | What's the difference between variance and standard deviation?
You don't need both. They each have different purposes. The SD is usually more useful to describe the variability of the data while the variance is usually much more useful mathematically. For example, the sum of uncorrelated distributions (random variab... | What's the difference between variance and standard deviation?
You don't need both. They each have different purposes. The SD is usually more useful to describe the variability of the data while the variance is usually much more useful mathematically. For exam |
818 | What's the difference between variance and standard deviation? | If John refers to independent random variables when he says "unrelated distributions," then his response is correct. However, to answer your question, there are several points that can be added:
The mean and variance are the two parameters that determine a normal distribution.
The Chebyshev inequality bounds the prob... | What's the difference between variance and standard deviation? | If John refers to independent random variables when he says "unrelated distributions," then his response is correct. However, to answer your question, there are several points that can be added:
The | What's the difference between variance and standard deviation?
If John refers to independent random variables when he says "unrelated distributions," then his response is correct. However, to answer your question, there are several points that can be added:
The mean and variance are the two parameters that determine ... | What's the difference between variance and standard deviation?
If John refers to independent random variables when he says "unrelated distributions," then his response is correct. However, to answer your question, there are several points that can be added:
The |
819 | What's the difference between variance and standard deviation? | The variance of a data set measures the mathematical dispersion of the data relative to the mean. However, though this value is theoretically correct, it is difficult to apply in a real-world sense because the values used to calculate it were squared. The standard deviation, as the square root of the variance gives a v... | What's the difference between variance and standard deviation? | The variance of a data set measures the mathematical dispersion of the data relative to the mean. However, though this value is theoretically correct, it is difficult to apply in a real-world sense be | What's the difference between variance and standard deviation?
The variance of a data set measures the mathematical dispersion of the data relative to the mean. However, though this value is theoretically correct, it is difficult to apply in a real-world sense because the values used to calculate it were squared. The s... | What's the difference between variance and standard deviation?
The variance of a data set measures the mathematical dispersion of the data relative to the mean. However, though this value is theoretically correct, it is difficult to apply in a real-world sense be |
820 | What's the difference between variance and standard deviation? | In terms of the distribution they're equivalent (yet obviously not interchangeable), but beware that in terms of estimators they're not: the square root of an estimate of the variance is NOT an (unbiased) estimator of the standard deviation. Only for a moderately large number of samples (and depending on the estimators... | What's the difference between variance and standard deviation? | In terms of the distribution they're equivalent (yet obviously not interchangeable), but beware that in terms of estimators they're not: the square root of an estimate of the variance is NOT an (unbia | What's the difference between variance and standard deviation?
In terms of the distribution they're equivalent (yet obviously not interchangeable), but beware that in terms of estimators they're not: the square root of an estimate of the variance is NOT an (unbiased) estimator of the standard deviation. Only for a mode... | What's the difference between variance and standard deviation?
In terms of the distribution they're equivalent (yet obviously not interchangeable), but beware that in terms of estimators they're not: the square root of an estimate of the variance is NOT an (unbia |
821 | What's the difference between variance and standard deviation? | While calculating the variance, we squared the deviations.
It mean that if the given data (observations) is in meters, it will become meter square.
Hope it's not correct representation about the deviations. So, we square root again (SD) that is nothing but SD. | What's the difference between variance and standard deviation? | While calculating the variance, we squared the deviations.
It mean that if the given data (observations) is in meters, it will become meter square.
Hope it's not correct representation about the devia | What's the difference between variance and standard deviation?
While calculating the variance, we squared the deviations.
It mean that if the given data (observations) is in meters, it will become meter square.
Hope it's not correct representation about the deviations. So, we square root again (SD) that is nothing but ... | What's the difference between variance and standard deviation?
While calculating the variance, we squared the deviations.
It mean that if the given data (observations) is in meters, it will become meter square.
Hope it's not correct representation about the devia |
822 | What's the difference between variance and standard deviation? | In adition to Hassan's response, you need to be careful on interpreting standard deviation. Some people define it as the mean distance between every observation and its mean, but this is the definition of mean absolute deviation (MAD), thus wrong.
For a better understanding of both concepts, variance and SD, I highly r... | What's the difference between variance and standard deviation? | In adition to Hassan's response, you need to be careful on interpreting standard deviation. Some people define it as the mean distance between every observation and its mean, but this is the definitio | What's the difference between variance and standard deviation?
In adition to Hassan's response, you need to be careful on interpreting standard deviation. Some people define it as the mean distance between every observation and its mean, but this is the definition of mean absolute deviation (MAD), thus wrong.
For a bet... | What's the difference between variance and standard deviation?
In adition to Hassan's response, you need to be careful on interpreting standard deviation. Some people define it as the mean distance between every observation and its mean, but this is the definitio |
823 | Cohen's kappa in plain English | Introduction
The Kappa statistic (or value) is a metric that compares an Observed Accuracy with an Expected Accuracy (random chance). The kappa statistic is used not only to evaluate a single classifier, but also to evaluate classifiers amongst themselves. In addition, it takes into account random chance (agreement wit... | Cohen's kappa in plain English | Introduction
The Kappa statistic (or value) is a metric that compares an Observed Accuracy with an Expected Accuracy (random chance). The kappa statistic is used not only to evaluate a single classifi | Cohen's kappa in plain English
Introduction
The Kappa statistic (or value) is a metric that compares an Observed Accuracy with an Expected Accuracy (random chance). The kappa statistic is used not only to evaluate a single classifier, but also to evaluate classifiers amongst themselves. In addition, it takes into accou... | Cohen's kappa in plain English
Introduction
The Kappa statistic (or value) is a metric that compares an Observed Accuracy with an Expected Accuracy (random chance). The kappa statistic is used not only to evaluate a single classifi |
824 | Cohen's kappa in plain English | rbx has a great answer. However, it is a little bit verbose. Here is my summary and intuition behind the Kappa metric.
Kappa is an important measure on classifier performance, especially on imbalanced data set.
For example, in credit card fraud detection, the marginal distribution of the response variable is high skew... | Cohen's kappa in plain English | rbx has a great answer. However, it is a little bit verbose. Here is my summary and intuition behind the Kappa metric.
Kappa is an important measure on classifier performance, especially on imbalance | Cohen's kappa in plain English
rbx has a great answer. However, it is a little bit verbose. Here is my summary and intuition behind the Kappa metric.
Kappa is an important measure on classifier performance, especially on imbalanced data set.
For example, in credit card fraud detection, the marginal distribution of the... | Cohen's kappa in plain English
rbx has a great answer. However, it is a little bit verbose. Here is my summary and intuition behind the Kappa metric.
Kappa is an important measure on classifier performance, especially on imbalance |
825 | Cohen's kappa in plain English | What value of Cohen's kappa is strong depends on several factors including for example, the number of categories or codes that are used affects kappa$^1$ and the probability that each code will be populated.
"For example, given equiprobable codes and observers who are 85% accurate:
value of kappa number of codes
0... | Cohen's kappa in plain English | What value of Cohen's kappa is strong depends on several factors including for example, the number of categories or codes that are used affects kappa$^1$ and the probability that each code will be pop | Cohen's kappa in plain English
What value of Cohen's kappa is strong depends on several factors including for example, the number of categories or codes that are used affects kappa$^1$ and the probability that each code will be populated.
"For example, given equiprobable codes and observers who are 85% accurate:
val... | Cohen's kappa in plain English
What value of Cohen's kappa is strong depends on several factors including for example, the number of categories or codes that are used affects kappa$^1$ and the probability that each code will be pop |
826 | Cohen's kappa in plain English | to answer your question (in plain english :-) ):
How does Kappa help in evaluating the prediction performance of classifiers? What does it tell?!!
You should consider the kappa as a measure of agreement between 2 individuals such that the result can be interpreted as:
Poor agreement = 0.20 or less
Fair agreement = 0... | Cohen's kappa in plain English | to answer your question (in plain english :-) ):
How does Kappa help in evaluating the prediction performance of classifiers? What does it tell?!!
You should consider the kappa as a measure of agre | Cohen's kappa in plain English
to answer your question (in plain english :-) ):
How does Kappa help in evaluating the prediction performance of classifiers? What does it tell?!!
You should consider the kappa as a measure of agreement between 2 individuals such that the result can be interpreted as:
Poor agreement = ... | Cohen's kappa in plain English
to answer your question (in plain english :-) ):
How does Kappa help in evaluating the prediction performance of classifiers? What does it tell?!!
You should consider the kappa as a measure of agre |
827 | When is it ok to remove the intercept in a linear regression model? | The shortest answer: never, unless you are sure that your linear approximation of the data generating process (linear regression model) either by some theoretical or any other reasons is forced to go through the origin. If not the other regression parameters will be biased even if intercept is statistically insignifica... | When is it ok to remove the intercept in a linear regression model? | The shortest answer: never, unless you are sure that your linear approximation of the data generating process (linear regression model) either by some theoretical or any other reasons is forced to go | When is it ok to remove the intercept in a linear regression model?
The shortest answer: never, unless you are sure that your linear approximation of the data generating process (linear regression model) either by some theoretical or any other reasons is forced to go through the origin. If not the other regression para... | When is it ok to remove the intercept in a linear regression model?
The shortest answer: never, unless you are sure that your linear approximation of the data generating process (linear regression model) either by some theoretical or any other reasons is forced to go |
828 | When is it ok to remove the intercept in a linear regression model? | Removing the intercept is a different model, but there are plenty of examples where it is legitimate. Answers so far have already discussed in detail the example where the true intercept is 0. I will focus on a few examples where we may be interested in an atypical model parametrization.
Example 1: The ANOVA-style Mo... | When is it ok to remove the intercept in a linear regression model? | Removing the intercept is a different model, but there are plenty of examples where it is legitimate. Answers so far have already discussed in detail the example where the true intercept is 0. I wil | When is it ok to remove the intercept in a linear regression model?
Removing the intercept is a different model, but there are plenty of examples where it is legitimate. Answers so far have already discussed in detail the example where the true intercept is 0. I will focus on a few examples where we may be interested... | When is it ok to remove the intercept in a linear regression model?
Removing the intercept is a different model, but there are plenty of examples where it is legitimate. Answers so far have already discussed in detail the example where the true intercept is 0. I wil |
829 | When is it ok to remove the intercept in a linear regression model? | There are good answers here. Two small things:
Regarding a higher $R^2$ when the intercept is dropped, you should read this excellent answer by @cardinal. (In short, statistical software sometimes uses a different definition for $R^2$ when the intercept is forced to 0. So the reported $R^2$ for models with and with... | When is it ok to remove the intercept in a linear regression model? | There are good answers here. Two small things:
Regarding a higher $R^2$ when the intercept is dropped, you should read this excellent answer by @cardinal. (In short, statistical software sometimes | When is it ok to remove the intercept in a linear regression model?
There are good answers here. Two small things:
Regarding a higher $R^2$ when the intercept is dropped, you should read this excellent answer by @cardinal. (In short, statistical software sometimes uses a different definition for $R^2$ when the inte... | When is it ok to remove the intercept in a linear regression model?
There are good answers here. Two small things:
Regarding a higher $R^2$ when the intercept is dropped, you should read this excellent answer by @cardinal. (In short, statistical software sometimes |
830 | When is it ok to remove the intercept in a linear regression model? | You shouldn't drop the intercept, regardless of whether you are likely or not to ever see all the explanatory variables having values of zero.
If you remove the intercept then the other estimates all become biased. Even if the true value of the intercept is approximately zero (which is all you can conclude from your d... | When is it ok to remove the intercept in a linear regression model? | You shouldn't drop the intercept, regardless of whether you are likely or not to ever see all the explanatory variables having values of zero.
If you remove the intercept then the other estimates all | When is it ok to remove the intercept in a linear regression model?
You shouldn't drop the intercept, regardless of whether you are likely or not to ever see all the explanatory variables having values of zero.
If you remove the intercept then the other estimates all become biased. Even if the true value of the interc... | When is it ok to remove the intercept in a linear regression model?
You shouldn't drop the intercept, regardless of whether you are likely or not to ever see all the explanatory variables having values of zero.
If you remove the intercept then the other estimates all |
831 | When is it ok to remove the intercept in a linear regression model? | OK, so you've changed the question a LOT
You can leave out the intercept when you know it's 0. That's it. And no, you can't do it because it's not significantly different from 0, you have to know it's 0 or your residuals are biased. And, in that case it is 0 so it won't make any difference if you leave it out... the... | When is it ok to remove the intercept in a linear regression model? | OK, so you've changed the question a LOT
You can leave out the intercept when you know it's 0. That's it. And no, you can't do it because it's not significantly different from 0, you have to know it | When is it ok to remove the intercept in a linear regression model?
OK, so you've changed the question a LOT
You can leave out the intercept when you know it's 0. That's it. And no, you can't do it because it's not significantly different from 0, you have to know it's 0 or your residuals are biased. And, in that cas... | When is it ok to remove the intercept in a linear regression model?
OK, so you've changed the question a LOT
You can leave out the intercept when you know it's 0. That's it. And no, you can't do it because it's not significantly different from 0, you have to know it |
832 | When is it ok to remove the intercept in a linear regression model? | Most multiple regression models include a constant term (i.e., the intercept), since this ensures that the model will be unbiased--i.e., the mean of the residuals will be exactly zero. (The coefficients in a regression model are estimated by least squares--i.e., minimizing the mean squared error. Now, the mean squared ... | When is it ok to remove the intercept in a linear regression model? | Most multiple regression models include a constant term (i.e., the intercept), since this ensures that the model will be unbiased--i.e., the mean of the residuals will be exactly zero. (The coefficien | When is it ok to remove the intercept in a linear regression model?
Most multiple regression models include a constant term (i.e., the intercept), since this ensures that the model will be unbiased--i.e., the mean of the residuals will be exactly zero. (The coefficients in a regression model are estimated by least squa... | When is it ok to remove the intercept in a linear regression model?
Most multiple regression models include a constant term (i.e., the intercept), since this ensures that the model will be unbiased--i.e., the mean of the residuals will be exactly zero. (The coefficien |
833 | When is it ok to remove the intercept in a linear regression model? | Short answer: (almost) NEVER. In the linear regression model
$$
y = \alpha + \beta x + \epsilon
$$,
if you set $\alpha=0$, then you say that you KNOW that the expected value of $y$ given $x=0$ is zero. You almost never know that.
$R^2$ becomes higher without intercept, not because the model is better, but because t... | When is it ok to remove the intercept in a linear regression model? | Short answer: (almost) NEVER. In the linear regression model
$$
y = \alpha + \beta x + \epsilon
$$,
if you set $\alpha=0$, then you say that you KNOW that the expected value of $y$ given $x=0$ is z | When is it ok to remove the intercept in a linear regression model?
Short answer: (almost) NEVER. In the linear regression model
$$
y = \alpha + \beta x + \epsilon
$$,
if you set $\alpha=0$, then you say that you KNOW that the expected value of $y$ given $x=0$ is zero. You almost never know that.
$R^2$ becomes high... | When is it ok to remove the intercept in a linear regression model?
Short answer: (almost) NEVER. In the linear regression model
$$
y = \alpha + \beta x + \epsilon
$$,
if you set $\alpha=0$, then you say that you KNOW that the expected value of $y$ given $x=0$ is z |
834 | When is it ok to remove the intercept in a linear regression model? | Full revision of my thoughts. Indeed dropping the intercept will cause a bias problem.
Have you considered centering your data so an intercept would have some meaning and avoid explaining how some (unreasonable) values could give negative values? If you adjust all three explanatory variables by subtract the mean sqrf... | When is it ok to remove the intercept in a linear regression model? | Full revision of my thoughts. Indeed dropping the intercept will cause a bias problem.
Have you considered centering your data so an intercept would have some meaning and avoid explaining how some (u | When is it ok to remove the intercept in a linear regression model?
Full revision of my thoughts. Indeed dropping the intercept will cause a bias problem.
Have you considered centering your data so an intercept would have some meaning and avoid explaining how some (unreasonable) values could give negative values? If ... | When is it ok to remove the intercept in a linear regression model?
Full revision of my thoughts. Indeed dropping the intercept will cause a bias problem.
Have you considered centering your data so an intercept would have some meaning and avoid explaining how some (u |
835 | When is it ok to remove the intercept in a linear regression model? | I just spent some time answering a similar question posted by someone else, but it was closed. There are some great answers here, but the answer I provide is a bit simpler. It might be more suited to people who have a weak understanding of regression.
Q1: How do I interpret the intercept in my model?
In regression mode... | When is it ok to remove the intercept in a linear regression model? | I just spent some time answering a similar question posted by someone else, but it was closed. There are some great answers here, but the answer I provide is a bit simpler. It might be more suited to | When is it ok to remove the intercept in a linear regression model?
I just spent some time answering a similar question posted by someone else, but it was closed. There are some great answers here, but the answer I provide is a bit simpler. It might be more suited to people who have a weak understanding of regression.
... | When is it ok to remove the intercept in a linear regression model?
I just spent some time answering a similar question posted by someone else, but it was closed. There are some great answers here, but the answer I provide is a bit simpler. It might be more suited to |
836 | When is it ok to remove the intercept in a linear regression model? | This question has already many answers and basically they relate to the two cases (the first and second point below). But the answers make not clear why it is the case. While pondering about this and thinking about why the intercept would be different from other regressors (why should we never exclude the intercept ter... | When is it ok to remove the intercept in a linear regression model? | This question has already many answers and basically they relate to the two cases (the first and second point below). But the answers make not clear why it is the case. While pondering about this and | When is it ok to remove the intercept in a linear regression model?
This question has already many answers and basically they relate to the two cases (the first and second point below). But the answers make not clear why it is the case. While pondering about this and thinking about why the intercept would be different ... | When is it ok to remove the intercept in a linear regression model?
This question has already many answers and basically they relate to the two cases (the first and second point below). But the answers make not clear why it is the case. While pondering about this and |
837 | Bottom to top explanation of the Mahalanobis distance? | Here is a scatterplot of some multivariate data (in two dimensions):
What can we make of it when the axes are left out?
Introduce coordinates that are suggested by the data themselves.
The origin will be at the centroid of the points (the point of their averages). The first coordinate axis (blue in the next figure) ... | Bottom to top explanation of the Mahalanobis distance? | Here is a scatterplot of some multivariate data (in two dimensions):
What can we make of it when the axes are left out?
Introduce coordinates that are suggested by the data themselves.
The origin wi | Bottom to top explanation of the Mahalanobis distance?
Here is a scatterplot of some multivariate data (in two dimensions):
What can we make of it when the axes are left out?
Introduce coordinates that are suggested by the data themselves.
The origin will be at the centroid of the points (the point of their averages)... | Bottom to top explanation of the Mahalanobis distance?
Here is a scatterplot of some multivariate data (in two dimensions):
What can we make of it when the axes are left out?
Introduce coordinates that are suggested by the data themselves.
The origin wi |
838 | Bottom to top explanation of the Mahalanobis distance? | My grandma cooks. Yours might too. Cooking is a delicious way to teach statistics.
Pumpkin Habanero cookies are awesome! Think about how wonderful cinnamon and ginger can be in Christmas treats, then realize how hot they are on their own.
The ingredients are:
3/4 c Pumpkin, canned
3/4 c Sugar, brown, light
1/2 c Sou... | Bottom to top explanation of the Mahalanobis distance? | My grandma cooks. Yours might too. Cooking is a delicious way to teach statistics.
Pumpkin Habanero cookies are awesome! Think about how wonderful cinnamon and ginger can be in Christmas treats, th | Bottom to top explanation of the Mahalanobis distance?
My grandma cooks. Yours might too. Cooking is a delicious way to teach statistics.
Pumpkin Habanero cookies are awesome! Think about how wonderful cinnamon and ginger can be in Christmas treats, then realize how hot they are on their own.
The ingredients are:
3/... | Bottom to top explanation of the Mahalanobis distance?
My grandma cooks. Yours might too. Cooking is a delicious way to teach statistics.
Pumpkin Habanero cookies are awesome! Think about how wonderful cinnamon and ginger can be in Christmas treats, th |
839 | Bottom to top explanation of the Mahalanobis distance? | I'd like to add a little technical information to Whuber's excellent answer. This information might not interest grandma, but perhaps her grandchild would find it helpful. The following is a bottom-to-top explanation of the relevant linear algebra.
Mahalanobis distance is defined as $d(x,y)=\sqrt{(x-y)^T\Sigma^{-1}(x-y... | Bottom to top explanation of the Mahalanobis distance? | I'd like to add a little technical information to Whuber's excellent answer. This information might not interest grandma, but perhaps her grandchild would find it helpful. The following is a bottom-to | Bottom to top explanation of the Mahalanobis distance?
I'd like to add a little technical information to Whuber's excellent answer. This information might not interest grandma, but perhaps her grandchild would find it helpful. The following is a bottom-to-top explanation of the relevant linear algebra.
Mahalanobis dist... | Bottom to top explanation of the Mahalanobis distance?
I'd like to add a little technical information to Whuber's excellent answer. This information might not interest grandma, but perhaps her grandchild would find it helpful. The following is a bottom-to |
840 | Bottom to top explanation of the Mahalanobis distance? | As a starting point, I would see the Mahalanobis distance as a suitable deformation of the usual Euclidean distance $d(x,y)=\sqrt{\langle x,y \rangle}$ between vectors $x$ and $y$ in $\mathbb R^{n}$. The extra piece of information here is that $x$ and $y$ are actually random vectors, i.e. 2 different realizations of a ... | Bottom to top explanation of the Mahalanobis distance? | As a starting point, I would see the Mahalanobis distance as a suitable deformation of the usual Euclidean distance $d(x,y)=\sqrt{\langle x,y \rangle}$ between vectors $x$ and $y$ in $\mathbb R^{n}$. | Bottom to top explanation of the Mahalanobis distance?
As a starting point, I would see the Mahalanobis distance as a suitable deformation of the usual Euclidean distance $d(x,y)=\sqrt{\langle x,y \rangle}$ between vectors $x$ and $y$ in $\mathbb R^{n}$. The extra piece of information here is that $x$ and $y$ are actua... | Bottom to top explanation of the Mahalanobis distance?
As a starting point, I would see the Mahalanobis distance as a suitable deformation of the usual Euclidean distance $d(x,y)=\sqrt{\langle x,y \rangle}$ between vectors $x$ and $y$ in $\mathbb R^{n}$. |
841 | Bottom to top explanation of the Mahalanobis distance? | Let's consider the two variables case. Seeing this picture of bivariate normal (thanks @whuber), you cannot simply claim that AB is larger than AC. There is a positive covariance; the two variables are related to each other.
You can apply simple Euclidean measurements (straight lines like AB and AC) only if the varia... | Bottom to top explanation of the Mahalanobis distance? | Let's consider the two variables case. Seeing this picture of bivariate normal (thanks @whuber), you cannot simply claim that AB is larger than AC. There is a positive covariance; the two variables a | Bottom to top explanation of the Mahalanobis distance?
Let's consider the two variables case. Seeing this picture of bivariate normal (thanks @whuber), you cannot simply claim that AB is larger than AC. There is a positive covariance; the two variables are related to each other.
You can apply simple Euclidean measure... | Bottom to top explanation of the Mahalanobis distance?
Let's consider the two variables case. Seeing this picture of bivariate normal (thanks @whuber), you cannot simply claim that AB is larger than AC. There is a positive covariance; the two variables a |
842 | Bottom to top explanation of the Mahalanobis distance? | I'll try to explain you as simply as possible:
Mahalanobis distance measures the distance of a point x from a data distribution.
The data distribution is characterized by a mean and the covariance matrix, thus is hypothesized as a multivariate gaussian.
It is used in pattern recognition as similarity measure between th... | Bottom to top explanation of the Mahalanobis distance? | I'll try to explain you as simply as possible:
Mahalanobis distance measures the distance of a point x from a data distribution.
The data distribution is characterized by a mean and the covariance mat | Bottom to top explanation of the Mahalanobis distance?
I'll try to explain you as simply as possible:
Mahalanobis distance measures the distance of a point x from a data distribution.
The data distribution is characterized by a mean and the covariance matrix, thus is hypothesized as a multivariate gaussian.
It is used ... | Bottom to top explanation of the Mahalanobis distance?
I'll try to explain you as simply as possible:
Mahalanobis distance measures the distance of a point x from a data distribution.
The data distribution is characterized by a mean and the covariance mat |
843 | Bottom to top explanation of the Mahalanobis distance? | I might be a bit late for answering this question. This paper in The Mahalanobis distance by MaesschalckD and Et. al. is a good start for understanding the Mahalanobis distance. They've provided a complete example with numerical values. What I like about it is the geometric representation of the problem is presented. | Bottom to top explanation of the Mahalanobis distance? | I might be a bit late for answering this question. This paper in The Mahalanobis distance by MaesschalckD and Et. al. is a good start for understanding the Mahalanobis distance. They've provided a com | Bottom to top explanation of the Mahalanobis distance?
I might be a bit late for answering this question. This paper in The Mahalanobis distance by MaesschalckD and Et. al. is a good start for understanding the Mahalanobis distance. They've provided a complete example with numerical values. What I like about it is the ... | Bottom to top explanation of the Mahalanobis distance?
I might be a bit late for answering this question. This paper in The Mahalanobis distance by MaesschalckD and Et. al. is a good start for understanding the Mahalanobis distance. They've provided a com |
844 | Bottom to top explanation of the Mahalanobis distance? | Just to add to the excellent explanations above, the Mahalanobis distance arises naturally in (multivariate) linear regression. This is a simple consequence of some of the connections between the Mahalanobis distance and the Gaussian distribution discussed in the other answers, but I think it's worth spelling out anywa... | Bottom to top explanation of the Mahalanobis distance? | Just to add to the excellent explanations above, the Mahalanobis distance arises naturally in (multivariate) linear regression. This is a simple consequence of some of the connections between the Maha | Bottom to top explanation of the Mahalanobis distance?
Just to add to the excellent explanations above, the Mahalanobis distance arises naturally in (multivariate) linear regression. This is a simple consequence of some of the connections between the Mahalanobis distance and the Gaussian distribution discussed in the o... | Bottom to top explanation of the Mahalanobis distance?
Just to add to the excellent explanations above, the Mahalanobis distance arises naturally in (multivariate) linear regression. This is a simple consequence of some of the connections between the Maha |
845 | Bottom to top explanation of the Mahalanobis distance? | Mahalanobis distance is an euclidian distance (natural distance) wich take into account the covariance of data.
It give a bigger weight to noisy component and so is very usefull to check for similarity between two datasets.
As you can see in your exemple here when variables are correlated, the distribution is shifted ... | Bottom to top explanation of the Mahalanobis distance? | Mahalanobis distance is an euclidian distance (natural distance) wich take into account the covariance of data.
It give a bigger weight to noisy component and so is very usefull to check for similarit | Bottom to top explanation of the Mahalanobis distance?
Mahalanobis distance is an euclidian distance (natural distance) wich take into account the covariance of data.
It give a bigger weight to noisy component and so is very usefull to check for similarity between two datasets.
As you can see in your exemple here when ... | Bottom to top explanation of the Mahalanobis distance?
Mahalanobis distance is an euclidian distance (natural distance) wich take into account the covariance of data.
It give a bigger weight to noisy component and so is very usefull to check for similarit |
846 | Bottom to top explanation of the Mahalanobis distance? | Standardise distance to the mean of normal distribution
In my understanding, Z-score and Mahalanobis distance are the standardising methods to measure the closeness to the mean of a distribution, where we need to consider the direction and variance of the dispersions.
Question
Which one, A or B, is closer to the mean ... | Bottom to top explanation of the Mahalanobis distance? | Standardise distance to the mean of normal distribution
In my understanding, Z-score and Mahalanobis distance are the standardising methods to measure the closeness to the mean of a distribution, whe | Bottom to top explanation of the Mahalanobis distance?
Standardise distance to the mean of normal distribution
In my understanding, Z-score and Mahalanobis distance are the standardising methods to measure the closeness to the mean of a distribution, where we need to consider the direction and variance of the dispersi... | Bottom to top explanation of the Mahalanobis distance?
Standardise distance to the mean of normal distribution
In my understanding, Z-score and Mahalanobis distance are the standardising methods to measure the closeness to the mean of a distribution, whe |
847 | Are large data sets inappropriate for hypothesis testing? | It is not true. If the null hypothesis is true then it will not be rejected more frequently at large sample sizes than small. There is an erroneous rejection rate that's usually set to 0.05 (alpha) but it is independent of sample size. Therefore, taken literally the statement is false. Nevertheless, it's possible that... | Are large data sets inappropriate for hypothesis testing? | It is not true. If the null hypothesis is true then it will not be rejected more frequently at large sample sizes than small. There is an erroneous rejection rate that's usually set to 0.05 (alpha) b | Are large data sets inappropriate for hypothesis testing?
It is not true. If the null hypothesis is true then it will not be rejected more frequently at large sample sizes than small. There is an erroneous rejection rate that's usually set to 0.05 (alpha) but it is independent of sample size. Therefore, taken literall... | Are large data sets inappropriate for hypothesis testing?
It is not true. If the null hypothesis is true then it will not be rejected more frequently at large sample sizes than small. There is an erroneous rejection rate that's usually set to 0.05 (alpha) b |
848 | Are large data sets inappropriate for hypothesis testing? | I agree with the answers that have appeared, but would like to add that perhaps the question could be redirected. Whether to test a hypothesis or not is a research question that ought, at least in general, be independent of how much data one has. If you really need to test a hypothesis, do so, and don't be afraid of ... | Are large data sets inappropriate for hypothesis testing? | I agree with the answers that have appeared, but would like to add that perhaps the question could be redirected. Whether to test a hypothesis or not is a research question that ought, at least in ge | Are large data sets inappropriate for hypothesis testing?
I agree with the answers that have appeared, but would like to add that perhaps the question could be redirected. Whether to test a hypothesis or not is a research question that ought, at least in general, be independent of how much data one has. If you really... | Are large data sets inappropriate for hypothesis testing?
I agree with the answers that have appeared, but would like to add that perhaps the question could be redirected. Whether to test a hypothesis or not is a research question that ought, at least in ge |
849 | Are large data sets inappropriate for hypothesis testing? | Hypothesis testing traditionally focused on p values to derive statistical significance when alpha is less than 0.05 has a major weakness. And, that is that with a large enough sample size any experiment can eventually reject the null hypothesis and detect trivially small differences that turn out to be statistically ... | Are large data sets inappropriate for hypothesis testing? | Hypothesis testing traditionally focused on p values to derive statistical significance when alpha is less than 0.05 has a major weakness. And, that is that with a large enough sample size any experi | Are large data sets inappropriate for hypothesis testing?
Hypothesis testing traditionally focused on p values to derive statistical significance when alpha is less than 0.05 has a major weakness. And, that is that with a large enough sample size any experiment can eventually reject the null hypothesis and detect triv... | Are large data sets inappropriate for hypothesis testing?
Hypothesis testing traditionally focused on p values to derive statistical significance when alpha is less than 0.05 has a major weakness. And, that is that with a large enough sample size any experi |
850 | Are large data sets inappropriate for hypothesis testing? | A (frequentist) hypothesis test, precisely, address the question of the probability of the observed data or something more extreme would be likely assuming the null hypothesis is true. This interpretation is indifferent to sample size. That interpretation is valid whether the sample is of size 5 or 1,000,000.
An impo... | Are large data sets inappropriate for hypothesis testing? | A (frequentist) hypothesis test, precisely, address the question of the probability of the observed data or something more extreme would be likely assuming the null hypothesis is true. This interpret | Are large data sets inappropriate for hypothesis testing?
A (frequentist) hypothesis test, precisely, address the question of the probability of the observed data or something more extreme would be likely assuming the null hypothesis is true. This interpretation is indifferent to sample size. That interpretation is v... | Are large data sets inappropriate for hypothesis testing?
A (frequentist) hypothesis test, precisely, address the question of the probability of the observed data or something more extreme would be likely assuming the null hypothesis is true. This interpret |
851 | Are large data sets inappropriate for hypothesis testing? | One simple point not made directly in another answer is that it's simply not true that "all null hypotheses are false."
The simple hypothesis that a physical coin has heads probability exactly equal to 0.5, ok, that is false.
But the compound hypothesis that a physical coin has heads probability greater than 0.499 and ... | Are large data sets inappropriate for hypothesis testing? | One simple point not made directly in another answer is that it's simply not true that "all null hypotheses are false."
The simple hypothesis that a physical coin has heads probability exactly equal t | Are large data sets inappropriate for hypothesis testing?
One simple point not made directly in another answer is that it's simply not true that "all null hypotheses are false."
The simple hypothesis that a physical coin has heads probability exactly equal to 0.5, ok, that is false.
But the compound hypothesis that a p... | Are large data sets inappropriate for hypothesis testing?
One simple point not made directly in another answer is that it's simply not true that "all null hypotheses are false."
The simple hypothesis that a physical coin has heads probability exactly equal t |
852 | Are large data sets inappropriate for hypothesis testing? | In a certain sense, [all] many null hypothesis are [always] false (the group of people living in houses with odd numbers does never exactly earn the same on average as the group of people living in houses with even numbers).
In the frequentist framework, the question that is asked is whether the difference in income b... | Are large data sets inappropriate for hypothesis testing? | In a certain sense, [all] many null hypothesis are [always] false (the group of people living in houses with odd numbers does never exactly earn the same on average as the group of people living in ho | Are large data sets inappropriate for hypothesis testing?
In a certain sense, [all] many null hypothesis are [always] false (the group of people living in houses with odd numbers does never exactly earn the same on average as the group of people living in houses with even numbers).
In the frequentist framework, the qu... | Are large data sets inappropriate for hypothesis testing?
In a certain sense, [all] many null hypothesis are [always] false (the group of people living in houses with odd numbers does never exactly earn the same on average as the group of people living in ho |
853 | Are large data sets inappropriate for hypothesis testing? | Hypothesis testing for large data should the desired level of difference into account, rather than whether there is a difference or not. You're not interested in the H0 that the estimate is exactly 0. A general approach would be to test whether the difference between the null hypothesis and the observed value is larger... | Are large data sets inappropriate for hypothesis testing? | Hypothesis testing for large data should the desired level of difference into account, rather than whether there is a difference or not. You're not interested in the H0 that the estimate is exactly 0. | Are large data sets inappropriate for hypothesis testing?
Hypothesis testing for large data should the desired level of difference into account, rather than whether there is a difference or not. You're not interested in the H0 that the estimate is exactly 0. A general approach would be to test whether the difference be... | Are large data sets inappropriate for hypothesis testing?
Hypothesis testing for large data should the desired level of difference into account, rather than whether there is a difference or not. You're not interested in the H0 that the estimate is exactly 0. |
854 | Are large data sets inappropriate for hypothesis testing? | "Does it mean that hypothesis testing is worthless for large data sets?"
No, it doesn't mean that. The general message is that decisions made after conducting a hypothesis test should always take into account the estimated effect size, and not only the p-value. Particularly, in experiments with very large sample sizes,... | Are large data sets inappropriate for hypothesis testing? | "Does it mean that hypothesis testing is worthless for large data sets?"
No, it doesn't mean that. The general message is that decisions made after conducting a hypothesis test should always take into | Are large data sets inappropriate for hypothesis testing?
"Does it mean that hypothesis testing is worthless for large data sets?"
No, it doesn't mean that. The general message is that decisions made after conducting a hypothesis test should always take into account the estimated effect size, and not only the p-value. ... | Are large data sets inappropriate for hypothesis testing?
"Does it mean that hypothesis testing is worthless for large data sets?"
No, it doesn't mean that. The general message is that decisions made after conducting a hypothesis test should always take into |
855 | Are large data sets inappropriate for hypothesis testing? | The short answer is "no". Research on hypothesis testing in the asymptotic regime of infinite observations and multiple hypotheses has been very, very active in the past 15-20 years, because of microarray data and financial data applications. The long answer is in the course page of Stat 329, "Large-Scale Simultaneous ... | Are large data sets inappropriate for hypothesis testing? | The short answer is "no". Research on hypothesis testing in the asymptotic regime of infinite observations and multiple hypotheses has been very, very active in the past 15-20 years, because of microa | Are large data sets inappropriate for hypothesis testing?
The short answer is "no". Research on hypothesis testing in the asymptotic regime of infinite observations and multiple hypotheses has been very, very active in the past 15-20 years, because of microarray data and financial data applications. The long answer is ... | Are large data sets inappropriate for hypothesis testing?
The short answer is "no". Research on hypothesis testing in the asymptotic regime of infinite observations and multiple hypotheses has been very, very active in the past 15-20 years, because of microa |
856 | Are large data sets inappropriate for hypothesis testing? | I think its a problem of most significance tests having some general undefined class of implicit alternatives to the null, which we never know. Often these classes may contain some sort of "sure thing" hypothesis, in which the data fits perfectly (i.e. a hypothesis of the form $H_{ST}:d_{1}=1.23,d_{2}=1.11,\dots$ wher... | Are large data sets inappropriate for hypothesis testing? | I think its a problem of most significance tests having some general undefined class of implicit alternatives to the null, which we never know. Often these classes may contain some sort of "sure thin | Are large data sets inappropriate for hypothesis testing?
I think its a problem of most significance tests having some general undefined class of implicit alternatives to the null, which we never know. Often these classes may contain some sort of "sure thing" hypothesis, in which the data fits perfectly (i.e. a hypoth... | Are large data sets inappropriate for hypothesis testing?
I think its a problem of most significance tests having some general undefined class of implicit alternatives to the null, which we never know. Often these classes may contain some sort of "sure thin |
857 | Are large data sets inappropriate for hypothesis testing? | No. It is true, that all useful point hypothesis tests are consistent and thus will show up a significant result if only the sample size is large enough and some irrelevant effect exists. To overcome this drawback of statistical hypotheses testing (already mentioned by the answer of Gaetan Lion above), there are releva... | Are large data sets inappropriate for hypothesis testing? | No. It is true, that all useful point hypothesis tests are consistent and thus will show up a significant result if only the sample size is large enough and some irrelevant effect exists. To overcome | Are large data sets inappropriate for hypothesis testing?
No. It is true, that all useful point hypothesis tests are consistent and thus will show up a significant result if only the sample size is large enough and some irrelevant effect exists. To overcome this drawback of statistical hypotheses testing (already menti... | Are large data sets inappropriate for hypothesis testing?
No. It is true, that all useful point hypothesis tests are consistent and thus will show up a significant result if only the sample size is large enough and some irrelevant effect exists. To overcome |
858 | Are large data sets inappropriate for hypothesis testing? | The article you mention does have a valid point, as far as standard frequentist tests are concerned. That is why testing for a given effect size is very important. To illustrate, here is an anova between 3 groups, where group B slightly different than group A and C. try this in r:
treat_diff=0.001 #size of treatment di... | Are large data sets inappropriate for hypothesis testing? | The article you mention does have a valid point, as far as standard frequentist tests are concerned. That is why testing for a given effect size is very important. To illustrate, here is an anova betw | Are large data sets inappropriate for hypothesis testing?
The article you mention does have a valid point, as far as standard frequentist tests are concerned. That is why testing for a given effect size is very important. To illustrate, here is an anova between 3 groups, where group B slightly different than group A an... | Are large data sets inappropriate for hypothesis testing?
The article you mention does have a valid point, as far as standard frequentist tests are concerned. That is why testing for a given effect size is very important. To illustrate, here is an anova betw |
859 | Are large data sets inappropriate for hypothesis testing? | I think what they mean is that one often makes an assumption about the probability density of the null hypothesis which has a 'simple' form but does not correspond to the true probability density.
Now with small data sets, you might not have enough sensitivity to see this effect but with a large enough data set you wi... | Are large data sets inappropriate for hypothesis testing? | I think what they mean is that one often makes an assumption about the probability density of the null hypothesis which has a 'simple' form but does not correspond to the true probability density.
No | Are large data sets inappropriate for hypothesis testing?
I think what they mean is that one often makes an assumption about the probability density of the null hypothesis which has a 'simple' form but does not correspond to the true probability density.
Now with small data sets, you might not have enough sensitivity ... | Are large data sets inappropriate for hypothesis testing?
I think what they mean is that one often makes an assumption about the probability density of the null hypothesis which has a 'simple' form but does not correspond to the true probability density.
No |
860 | Are large data sets inappropriate for hypothesis testing? | Isn't all this a matter of type I error versus type II error (or power) ? If one fixes the type I error probability ($\alpha$) at 0.05, then , obviously (except in the discrete case), it will be 0.05 whether the sample is large or not.
But for a given type I error probability, 0.05 e.g., the power, or the probability... | Are large data sets inappropriate for hypothesis testing? | Isn't all this a matter of type I error versus type II error (or power) ? If one fixes the type I error probability ($\alpha$) at 0.05, then , obviously (except in the discrete case), it will be 0.05 | Are large data sets inappropriate for hypothesis testing?
Isn't all this a matter of type I error versus type II error (or power) ? If one fixes the type I error probability ($\alpha$) at 0.05, then , obviously (except in the discrete case), it will be 0.05 whether the sample is large or not.
But for a given type I e... | Are large data sets inappropriate for hypothesis testing?
Isn't all this a matter of type I error versus type II error (or power) ? If one fixes the type I error probability ($\alpha$) at 0.05, then , obviously (except in the discrete case), it will be 0.05 |
861 | Are large data sets inappropriate for hypothesis testing? | "We know that for large enough sample sizes, every study—including ones in which the null hypothesis of no effect is true—will declare a statistically significant effect".
Well, in a sense all (most) Null hypothesis are false. The parameter under consideration has to be equal to the hypothesized value right down to an... | Are large data sets inappropriate for hypothesis testing? | "We know that for large enough sample sizes, every study—including ones in which the null hypothesis of no effect is true—will declare a statistically significant effect".
Well, in a sense all (most) | Are large data sets inappropriate for hypothesis testing?
"We know that for large enough sample sizes, every study—including ones in which the null hypothesis of no effect is true—will declare a statistically significant effect".
Well, in a sense all (most) Null hypothesis are false. The parameter under consideration ... | Are large data sets inappropriate for hypothesis testing?
"We know that for large enough sample sizes, every study—including ones in which the null hypothesis of no effect is true—will declare a statistically significant effect".
Well, in a sense all (most) |
862 | Are large data sets inappropriate for hypothesis testing? | This is a critic of Bayesian inference, a different way to thing statistics (different from frequentists form that everyone learns in others courses others than statistics, we learn both).
"The critic is that you can proof anything with a large sample because gives to you a p-value."
That's why we look in A LOT of othe... | Are large data sets inappropriate for hypothesis testing? | This is a critic of Bayesian inference, a different way to thing statistics (different from frequentists form that everyone learns in others courses others than statistics, we learn both).
"The critic | Are large data sets inappropriate for hypothesis testing?
This is a critic of Bayesian inference, a different way to thing statistics (different from frequentists form that everyone learns in others courses others than statistics, we learn both).
"The critic is that you can proof anything with a large sample because gi... | Are large data sets inappropriate for hypothesis testing?
This is a critic of Bayesian inference, a different way to thing statistics (different from frequentists form that everyone learns in others courses others than statistics, we learn both).
"The critic |
863 | Deriving the conditional distributions of a multivariate normal distribution | You can prove it by explicitly calculating the conditional density by brute force, as in Procrastinator's link (+1) in the comments. But, there's also a theorem that says all conditional distributions of a multivariate normal distribution are normal. Therefore, all that's left is to calculate the mean vector and covari... | Deriving the conditional distributions of a multivariate normal distribution | You can prove it by explicitly calculating the conditional density by brute force, as in Procrastinator's link (+1) in the comments. But, there's also a theorem that says all conditional distributions | Deriving the conditional distributions of a multivariate normal distribution
You can prove it by explicitly calculating the conditional density by brute force, as in Procrastinator's link (+1) in the comments. But, there's also a theorem that says all conditional distributions of a multivariate normal distribution are ... | Deriving the conditional distributions of a multivariate normal distribution
You can prove it by explicitly calculating the conditional density by brute force, as in Procrastinator's link (+1) in the comments. But, there's also a theorem that says all conditional distributions |
864 | Deriving the conditional distributions of a multivariate normal distribution | The answer by Macro is great, but here is an even simpler way that does not require you to use any outside theorem asserting the conditional distribution. It involves writing the Mahalanobis distance in a form that separates the argument variable for the conditioning statement, and then factorising the normal density ... | Deriving the conditional distributions of a multivariate normal distribution | The answer by Macro is great, but here is an even simpler way that does not require you to use any outside theorem asserting the conditional distribution. It involves writing the Mahalanobis distance | Deriving the conditional distributions of a multivariate normal distribution
The answer by Macro is great, but here is an even simpler way that does not require you to use any outside theorem asserting the conditional distribution. It involves writing the Mahalanobis distance in a form that separates the argument vari... | Deriving the conditional distributions of a multivariate normal distribution
The answer by Macro is great, but here is an even simpler way that does not require you to use any outside theorem asserting the conditional distribution. It involves writing the Mahalanobis distance |
865 | What is the difference between a neural network and a deep neural network, and why do the deep ones work better? | Let's start with a triviliaty: Deep neural network is simply a feedforward network with many hidden layers.
This is more or less all there is to say about the definition. Neural networks can be recurrent or feedforward; feedforward ones do not have any loops in their graph and can be organized in layers. If there are "... | What is the difference between a neural network and a deep neural network, and why do the deep ones | Let's start with a triviliaty: Deep neural network is simply a feedforward network with many hidden layers.
This is more or less all there is to say about the definition. Neural networks can be recurr | What is the difference between a neural network and a deep neural network, and why do the deep ones work better?
Let's start with a triviliaty: Deep neural network is simply a feedforward network with many hidden layers.
This is more or less all there is to say about the definition. Neural networks can be recurrent or ... | What is the difference between a neural network and a deep neural network, and why do the deep ones
Let's start with a triviliaty: Deep neural network is simply a feedforward network with many hidden layers.
This is more or less all there is to say about the definition. Neural networks can be recurr |
866 | What is the difference between a neural network and a deep neural network, and why do the deep ones work better? | Good answer so far, though there are a couple of things nobody around here mentioned, here's my 0.02$
I'll just answer in the form of a story, should make things more fun and clear. No tldr here. In the process you should be able to understand what the difference is.
There are multiple reasons why DNNs sparked when the... | What is the difference between a neural network and a deep neural network, and why do the deep ones | Good answer so far, though there are a couple of things nobody around here mentioned, here's my 0.02$
I'll just answer in the form of a story, should make things more fun and clear. No tldr here. In t | What is the difference between a neural network and a deep neural network, and why do the deep ones work better?
Good answer so far, though there are a couple of things nobody around here mentioned, here's my 0.02$
I'll just answer in the form of a story, should make things more fun and clear. No tldr here. In the proc... | What is the difference between a neural network and a deep neural network, and why do the deep ones
Good answer so far, though there are a couple of things nobody around here mentioned, here's my 0.02$
I'll just answer in the form of a story, should make things more fun and clear. No tldr here. In t |
867 | What is the difference between a neural network and a deep neural network, and why do the deep ones work better? | In layman terms, the main difference with the classic Neural Networks is that they have much more hidden layers.
The idea is to add labels to the layers to make several layers of abstraction:
For example, a deep neural network for object recognition:
Layer 1: Single pixels
Layer 2: Edges
Layer 3: Forms(circles, squa... | What is the difference between a neural network and a deep neural network, and why do the deep ones | In layman terms, the main difference with the classic Neural Networks is that they have much more hidden layers.
The idea is to add labels to the layers to make several layers of abstraction:
For exam | What is the difference between a neural network and a deep neural network, and why do the deep ones work better?
In layman terms, the main difference with the classic Neural Networks is that they have much more hidden layers.
The idea is to add labels to the layers to make several layers of abstraction:
For example, a ... | What is the difference between a neural network and a deep neural network, and why do the deep ones
In layman terms, the main difference with the classic Neural Networks is that they have much more hidden layers.
The idea is to add labels to the layers to make several layers of abstraction:
For exam |
868 | What is the difference between a neural network and a deep neural network, and why do the deep ones work better? | NN:
one hidden layer is enough but can have multiple layers nevertheless, left to right ordering (model: feed forward NN)
trained only in supervised way (backpropagation)
when multiple layers are used, train all the layers at the same time (same algorithm: backpropagation), more layers makes it difficult to use as err... | What is the difference between a neural network and a deep neural network, and why do the deep ones | NN:
one hidden layer is enough but can have multiple layers nevertheless, left to right ordering (model: feed forward NN)
trained only in supervised way (backpropagation)
when multiple layers are use | What is the difference between a neural network and a deep neural network, and why do the deep ones work better?
NN:
one hidden layer is enough but can have multiple layers nevertheless, left to right ordering (model: feed forward NN)
trained only in supervised way (backpropagation)
when multiple layers are used, trai... | What is the difference between a neural network and a deep neural network, and why do the deep ones
NN:
one hidden layer is enough but can have multiple layers nevertheless, left to right ordering (model: feed forward NN)
trained only in supervised way (backpropagation)
when multiple layers are use |
869 | What is the difference between a neural network and a deep neural network, and why do the deep ones work better? | To expand on David Gasquez's answer, one of the main differences between deep neural networks and traditional neural networks is that we don't just use backpropagation for deep neural nets.
Why? Because backpropagation trains later layers more efficiently than it trains earlier layers--as you go earlier and earlier in ... | What is the difference between a neural network and a deep neural network, and why do the deep ones | To expand on David Gasquez's answer, one of the main differences between deep neural networks and traditional neural networks is that we don't just use backpropagation for deep neural nets.
Why? Becau | What is the difference between a neural network and a deep neural network, and why do the deep ones work better?
To expand on David Gasquez's answer, one of the main differences between deep neural networks and traditional neural networks is that we don't just use backpropagation for deep neural nets.
Why? Because back... | What is the difference between a neural network and a deep neural network, and why do the deep ones
To expand on David Gasquez's answer, one of the main differences between deep neural networks and traditional neural networks is that we don't just use backpropagation for deep neural nets.
Why? Becau |
870 | What is the difference between a neural network and a deep neural network, and why do the deep ones work better? | I've also been confused a bit in the beginning by the difference between neural networks (NN) and deep neural networks (DNN), however the 'depth' refers only to the number of parameters & layers, unfortunately. You can take it as some sort of re-branding under the so-called 'Canadian Mafia'.
Several years ago, I also h... | What is the difference between a neural network and a deep neural network, and why do the deep ones | I've also been confused a bit in the beginning by the difference between neural networks (NN) and deep neural networks (DNN), however the 'depth' refers only to the number of parameters & layers, unfo | What is the difference between a neural network and a deep neural network, and why do the deep ones work better?
I've also been confused a bit in the beginning by the difference between neural networks (NN) and deep neural networks (DNN), however the 'depth' refers only to the number of parameters & layers, unfortunate... | What is the difference between a neural network and a deep neural network, and why do the deep ones
I've also been confused a bit in the beginning by the difference between neural networks (NN) and deep neural networks (DNN), however the 'depth' refers only to the number of parameters & layers, unfo |
871 | What is the difference between a neural network and a deep neural network, and why do the deep ones work better? | As far as I know, what is called Deep Neural Network (DNN) today has nothing fundamentally or philosophically different from the old standard Neural Network (NN). Although, in theory, one can approximate an arbitrary NN using a shallow NN with only one hidden layer, however, this does not mean that the two networks wil... | What is the difference between a neural network and a deep neural network, and why do the deep ones | As far as I know, what is called Deep Neural Network (DNN) today has nothing fundamentally or philosophically different from the old standard Neural Network (NN). Although, in theory, one can approxim | What is the difference between a neural network and a deep neural network, and why do the deep ones work better?
As far as I know, what is called Deep Neural Network (DNN) today has nothing fundamentally or philosophically different from the old standard Neural Network (NN). Although, in theory, one can approximate an ... | What is the difference between a neural network and a deep neural network, and why do the deep ones
As far as I know, what is called Deep Neural Network (DNN) today has nothing fundamentally or philosophically different from the old standard Neural Network (NN). Although, in theory, one can approxim |
872 | What is the difference between a neural network and a deep neural network, and why do the deep ones work better? | The difference between a "Deep" NN and a standard NN is purely qualitative: there is no definition of what that "Deep" means. "Deep" can mean anything from the extremely sophisticated architectures that are used by Google, Facebook and co which have 50-80 or even more layers, to 2 hidden layers (4 layers total) archite... | What is the difference between a neural network and a deep neural network, and why do the deep ones | The difference between a "Deep" NN and a standard NN is purely qualitative: there is no definition of what that "Deep" means. "Deep" can mean anything from the extremely sophisticated architectures th | What is the difference between a neural network and a deep neural network, and why do the deep ones work better?
The difference between a "Deep" NN and a standard NN is purely qualitative: there is no definition of what that "Deep" means. "Deep" can mean anything from the extremely sophisticated architectures that are ... | What is the difference between a neural network and a deep neural network, and why do the deep ones
The difference between a "Deep" NN and a standard NN is purely qualitative: there is no definition of what that "Deep" means. "Deep" can mean anything from the extremely sophisticated architectures th |
873 | What is the difference between a neural network and a deep neural network, and why do the deep ones work better? | I wouldn't say there is any big philosophical difference between NN and DNN (in fact I would say DNN is just a marketing term to distinguish from 'failed' NN) . What has changed is the size of the data sets. Essentially neural networks are currently the best $O(n)$ statistical estimators, working well for high dimens... | What is the difference between a neural network and a deep neural network, and why do the deep ones | I wouldn't say there is any big philosophical difference between NN and DNN (in fact I would say DNN is just a marketing term to distinguish from 'failed' NN) . What has changed is the size of the d | What is the difference between a neural network and a deep neural network, and why do the deep ones work better?
I wouldn't say there is any big philosophical difference between NN and DNN (in fact I would say DNN is just a marketing term to distinguish from 'failed' NN) . What has changed is the size of the data set... | What is the difference between a neural network and a deep neural network, and why do the deep ones
I wouldn't say there is any big philosophical difference between NN and DNN (in fact I would say DNN is just a marketing term to distinguish from 'failed' NN) . What has changed is the size of the d |
874 | What is the difference between a neural network and a deep neural network, and why do the deep ones work better? | To answer the latter question, look at this paper from Telgarsky which says that for a certain classification problem "all shallow networks with fewer than exponentially (in k) many nodes exhibit error at least 1/6, whereas a deep network with 2 nodes in each of 2k layers achieves zero error."
The classification probl... | What is the difference between a neural network and a deep neural network, and why do the deep ones | To answer the latter question, look at this paper from Telgarsky which says that for a certain classification problem "all shallow networks with fewer than exponentially (in k) many nodes exhibit erro | What is the difference between a neural network and a deep neural network, and why do the deep ones work better?
To answer the latter question, look at this paper from Telgarsky which says that for a certain classification problem "all shallow networks with fewer than exponentially (in k) many nodes exhibit error at le... | What is the difference between a neural network and a deep neural network, and why do the deep ones
To answer the latter question, look at this paper from Telgarsky which says that for a certain classification problem "all shallow networks with fewer than exponentially (in k) many nodes exhibit erro |
875 | What is the difference between a neural network and a deep neural network, and why do the deep ones work better? | Deep Learning is a set of algorithms in machine learning that attempt to model high-level abstractions in data by using architectures composed of multiple non-linear transformations.
Source: Arno Candel | What is the difference between a neural network and a deep neural network, and why do the deep ones | Deep Learning is a set of algorithms in machine learning that attempt to model high-level abstractions in data by using architectures composed of multiple non-linear transformations.
Source: Arno Can | What is the difference between a neural network and a deep neural network, and why do the deep ones work better?
Deep Learning is a set of algorithms in machine learning that attempt to model high-level abstractions in data by using architectures composed of multiple non-linear transformations.
Source: Arno Candel | What is the difference between a neural network and a deep neural network, and why do the deep ones
Deep Learning is a set of algorithms in machine learning that attempt to model high-level abstractions in data by using architectures composed of multiple non-linear transformations.
Source: Arno Can |
876 | What is the difference between off-policy and on-policy learning? | First of all, there's no reason that an agent has to do the greedy action; Agents can explore or they can follow options. This is not what separates on-policy from off-policy learning.
The reason that Q-learning is off-policy is that it updates its Q-values using the Q-value of the next state $s'$ and the greedy act... | What is the difference between off-policy and on-policy learning? | First of all, there's no reason that an agent has to do the greedy action; Agents can explore or they can follow options. This is not what separates on-policy from off-policy learning.
The reason t | What is the difference between off-policy and on-policy learning?
First of all, there's no reason that an agent has to do the greedy action; Agents can explore or they can follow options. This is not what separates on-policy from off-policy learning.
The reason that Q-learning is off-policy is that it updates its Q-... | What is the difference between off-policy and on-policy learning?
First of all, there's no reason that an agent has to do the greedy action; Agents can explore or they can follow options. This is not what separates on-policy from off-policy learning.
The reason t |
877 | What is the difference between off-policy and on-policy learning? | First of all, what does policy (denoted by $\pi$) actually mean?
Policy specifies an action $a$, that is taken in a state $s$ (or more precisely, $\pi$ is a probability, that an action $a$ is taken in a state $s$).
Second, what types of learning do we have?
Evaluate $Q(s,a)$ function: predict sum of future discounted ... | What is the difference between off-policy and on-policy learning? | First of all, what does policy (denoted by $\pi$) actually mean?
Policy specifies an action $a$, that is taken in a state $s$ (or more precisely, $\pi$ is a probability, that an action $a$ is taken in | What is the difference between off-policy and on-policy learning?
First of all, what does policy (denoted by $\pi$) actually mean?
Policy specifies an action $a$, that is taken in a state $s$ (or more precisely, $\pi$ is a probability, that an action $a$ is taken in a state $s$).
Second, what types of learning do we ha... | What is the difference between off-policy and on-policy learning?
First of all, what does policy (denoted by $\pi$) actually mean?
Policy specifies an action $a$, that is taken in a state $s$ (or more precisely, $\pi$ is a probability, that an action $a$ is taken in |
878 | What is the difference between off-policy and on-policy learning? | On-policy methods estimate the value of a policy while using it for control.
In off-policy methods, the policy used to generate behaviour, called the behaviour policy, may be unrelated to the policy that is evaluated and improved, called the estimation policy.
An advantage of this seperation is that the estimation po... | What is the difference between off-policy and on-policy learning? | On-policy methods estimate the value of a policy while using it for control.
In off-policy methods, the policy used to generate behaviour, called the behaviour policy, may be unrelated to the policy | What is the difference between off-policy and on-policy learning?
On-policy methods estimate the value of a policy while using it for control.
In off-policy methods, the policy used to generate behaviour, called the behaviour policy, may be unrelated to the policy that is evaluated and improved, called the estimation ... | What is the difference between off-policy and on-policy learning?
On-policy methods estimate the value of a policy while using it for control.
In off-policy methods, the policy used to generate behaviour, called the behaviour policy, may be unrelated to the policy |
879 | What is the difference between off-policy and on-policy learning? | The difference between Off-policy and On-policy methods is that with the first you do not need to follow any specific policy, your agent could even behave randomly and despite this, off-policy methods can still find the optimal policy. On the other hand on-policy methods are dependent on the policy used. In the case of... | What is the difference between off-policy and on-policy learning? | The difference between Off-policy and On-policy methods is that with the first you do not need to follow any specific policy, your agent could even behave randomly and despite this, off-policy methods | What is the difference between off-policy and on-policy learning?
The difference between Off-policy and On-policy methods is that with the first you do not need to follow any specific policy, your agent could even behave randomly and despite this, off-policy methods can still find the optimal policy. On the other hand ... | What is the difference between off-policy and on-policy learning?
The difference between Off-policy and On-policy methods is that with the first you do not need to follow any specific policy, your agent could even behave randomly and despite this, off-policy methods |
880 | What is the difference between off-policy and on-policy learning? | On-policy learning: The same (ϵ-greedy) policy that is evaluated and improved is also used to select actions. For eg. SARSA TD Learning Algorithm
Off-policy learning: The (greedy) policy that is evaluated and improved is different from the (ϵ-greedy) policy that is used to select actions. For eg. Q-Learning Algorithm | What is the difference between off-policy and on-policy learning? | On-policy learning: The same (ϵ-greedy) policy that is evaluated and improved is also used to select actions. For eg. SARSA TD Learning Algorithm
Off-policy learning: The (greedy) policy that is evalu | What is the difference between off-policy and on-policy learning?
On-policy learning: The same (ϵ-greedy) policy that is evaluated and improved is also used to select actions. For eg. SARSA TD Learning Algorithm
Off-policy learning: The (greedy) policy that is evaluated and improved is different from the (ϵ-greedy) pol... | What is the difference between off-policy and on-policy learning?
On-policy learning: The same (ϵ-greedy) policy that is evaluated and improved is also used to select actions. For eg. SARSA TD Learning Algorithm
Off-policy learning: The (greedy) policy that is evalu |
881 | What is the difference between off-policy and on-policy learning? | From the Sutton book: "The on-policy approach in the preceding section is actually a compromise—it learns action values not for the optimal policy, but for a near-optimal policy that still explores. A more straightforward approach is to use two policies, one that is learned about and that becomes the optimal policy, an... | What is the difference between off-policy and on-policy learning? | From the Sutton book: "The on-policy approach in the preceding section is actually a compromise—it learns action values not for the optimal policy, but for a near-optimal policy that still explores. A | What is the difference between off-policy and on-policy learning?
From the Sutton book: "The on-policy approach in the preceding section is actually a compromise—it learns action values not for the optimal policy, but for a near-optimal policy that still explores. A more straightforward approach is to use two policies,... | What is the difference between off-policy and on-policy learning?
From the Sutton book: "The on-policy approach in the preceding section is actually a compromise—it learns action values not for the optimal policy, but for a near-optimal policy that still explores. A |
882 | What is the difference between off-policy and on-policy learning? | This is the recursive version of the Q-function (according to Bellman equation):
$$Q_\pi(s_t,a_t)=\mathbb{E}_{\,r_t,\,s_{t+1}\,\sim\,E}\left[r(s_t,a_t)+\gamma\,\mathbb{E}_{\,a_{t+1}\,\sim\,\pi}\left[Q_\pi(s_{t+1}, a_{t+1})\right]\right]$$
Notice that the outer expectation exists because the current reward and the next ... | What is the difference between off-policy and on-policy learning? | This is the recursive version of the Q-function (according to Bellman equation):
$$Q_\pi(s_t,a_t)=\mathbb{E}_{\,r_t,\,s_{t+1}\,\sim\,E}\left[r(s_t,a_t)+\gamma\,\mathbb{E}_{\,a_{t+1}\,\sim\,\pi}\left[Q | What is the difference between off-policy and on-policy learning?
This is the recursive version of the Q-function (according to Bellman equation):
$$Q_\pi(s_t,a_t)=\mathbb{E}_{\,r_t,\,s_{t+1}\,\sim\,E}\left[r(s_t,a_t)+\gamma\,\mathbb{E}_{\,a_{t+1}\,\sim\,\pi}\left[Q_\pi(s_{t+1}, a_{t+1})\right]\right]$$
Notice that the... | What is the difference between off-policy and on-policy learning?
This is the recursive version of the Q-function (according to Bellman equation):
$$Q_\pi(s_t,a_t)=\mathbb{E}_{\,r_t,\,s_{t+1}\,\sim\,E}\left[r(s_t,a_t)+\gamma\,\mathbb{E}_{\,a_{t+1}\,\sim\,\pi}\left[Q |
883 | What is the difference between off-policy and on-policy learning? | On-policy methods attempt to evaluate or improve the
policy that is used to make decisions, whereas off-policy methods evaluate or improve
a policy different from that used to generate the data. [1]
[1]. Reinforcement Learning: An Introduction. Second edition, in progress. Richard S. Sutton and Andrew G. Barto c 2014,... | What is the difference between off-policy and on-policy learning? | On-policy methods attempt to evaluate or improve the
policy that is used to make decisions, whereas off-policy methods evaluate or improve
a policy different from that used to generate the data. [1]
| What is the difference between off-policy and on-policy learning?
On-policy methods attempt to evaluate or improve the
policy that is used to make decisions, whereas off-policy methods evaluate or improve
a policy different from that used to generate the data. [1]
[1]. Reinforcement Learning: An Introduction. Second e... | What is the difference between off-policy and on-policy learning?
On-policy methods attempt to evaluate or improve the
policy that is used to make decisions, whereas off-policy methods evaluate or improve
a policy different from that used to generate the data. [1]
|
884 | What is the difference between off-policy and on-policy learning? | In the way I understood it, hope it helps :
On-policy learning updates the policy currently in use while off-policy learning updates a different policy using the data collected from a different policy.
On-policy learning is a type of RL that updates the policy being used to take actions as the agent interacts with the... | What is the difference between off-policy and on-policy learning? | In the way I understood it, hope it helps :
On-policy learning updates the policy currently in use while off-policy learning updates a different policy using the data collected from a different policy | What is the difference between off-policy and on-policy learning?
In the way I understood it, hope it helps :
On-policy learning updates the policy currently in use while off-policy learning updates a different policy using the data collected from a different policy.
On-policy learning is a type of RL that updates the... | What is the difference between off-policy and on-policy learning?
In the way I understood it, hope it helps :
On-policy learning updates the policy currently in use while off-policy learning updates a different policy using the data collected from a different policy |
885 | What is the difference between off-policy and on-policy learning? | I find this helpful:
Michael Herrmann: On-Policy and Off-Policy Algorithm | What is the difference between off-policy and on-policy learning? | I find this helpful:
Michael Herrmann: On-Policy and Off-Policy Algorithm | What is the difference between off-policy and on-policy learning?
I find this helpful:
Michael Herrmann: On-Policy and Off-Policy Algorithm | What is the difference between off-policy and on-policy learning?
I find this helpful:
Michael Herrmann: On-Policy and Off-Policy Algorithm |
886 | Crossed vs nested random effects: how do they differ and how are they specified correctly in lme4? | (This is a fairly long answer, there is a summary at the end)
You are not wrong in your understanding of what nested and crossed random effects are in the scenario that you describe. However, your definition of crossed random effects is a little narrow. A more general definition of crossed random effects is simply: not... | Crossed vs nested random effects: how do they differ and how are they specified correctly in lme4? | (This is a fairly long answer, there is a summary at the end)
You are not wrong in your understanding of what nested and crossed random effects are in the scenario that you describe. However, your def | Crossed vs nested random effects: how do they differ and how are they specified correctly in lme4?
(This is a fairly long answer, there is a summary at the end)
You are not wrong in your understanding of what nested and crossed random effects are in the scenario that you describe. However, your definition of crossed ra... | Crossed vs nested random effects: how do they differ and how are they specified correctly in lme4?
(This is a fairly long answer, there is a summary at the end)
You are not wrong in your understanding of what nested and crossed random effects are in the scenario that you describe. However, your def |
887 | What's the difference between Normalization and Standardization? | Normalization rescales the values into a range of [0,1]. This might be useful in some cases where all parameters need to have the same positive scale. However, the outliers from the data set are lost.
$$ X_{changed} = \frac{X - X_{min}}{X_{max}-X_{min}} $$
Standardization rescales data to have a mean ($\mu$) of 0 and... | What's the difference between Normalization and Standardization? | Normalization rescales the values into a range of [0,1]. This might be useful in some cases where all parameters need to have the same positive scale. However, the outliers from the data set are lost | What's the difference between Normalization and Standardization?
Normalization rescales the values into a range of [0,1]. This might be useful in some cases where all parameters need to have the same positive scale. However, the outliers from the data set are lost.
$$ X_{changed} = \frac{X - X_{min}}{X_{max}-X_{min}} ... | What's the difference between Normalization and Standardization?
Normalization rescales the values into a range of [0,1]. This might be useful in some cases where all parameters need to have the same positive scale. However, the outliers from the data set are lost |
888 | What's the difference between Normalization and Standardization? | In the business world, "normalization" typically means that the range of values are "normalized to be from 0.0 to 1.0". "Standardization" typically means that the range of values are "standardized" to measure how many standard deviations the value is from its mean. However, not everyone would agree with that. It... | What's the difference between Normalization and Standardization? | In the business world, "normalization" typically means that the range of values are "normalized to be from 0.0 to 1.0". "Standardization" typically means that the range of values are "standardized" | What's the difference between Normalization and Standardization?
In the business world, "normalization" typically means that the range of values are "normalized to be from 0.0 to 1.0". "Standardization" typically means that the range of values are "standardized" to measure how many standard deviations the value is f... | What's the difference between Normalization and Standardization?
In the business world, "normalization" typically means that the range of values are "normalized to be from 0.0 to 1.0". "Standardization" typically means that the range of values are "standardized" |
889 | What's the difference between Normalization and Standardization? | The answer is simple, but you're not going to like it: it depends. If you value 1 standard deviation from both scores equally, then standardization is the way to go (note: in fact, you're studentizing, because you're dividing by an estimate of the SD of the population).
If not, it is likely that standardization will be... | What's the difference between Normalization and Standardization? | The answer is simple, but you're not going to like it: it depends. If you value 1 standard deviation from both scores equally, then standardization is the way to go (note: in fact, you're studentizing | What's the difference between Normalization and Standardization?
The answer is simple, but you're not going to like it: it depends. If you value 1 standard deviation from both scores equally, then standardization is the way to go (note: in fact, you're studentizing, because you're dividing by an estimate of the SD of t... | What's the difference between Normalization and Standardization?
The answer is simple, but you're not going to like it: it depends. If you value 1 standard deviation from both scores equally, then standardization is the way to go (note: in fact, you're studentizing |
890 | What's the difference between Normalization and Standardization? | To solve the GPA/ACT or train/car problem, why not use the Geometric Mean?
n√(a1 × a2 × ... × an)
Where a* is the value from the distribution and n is the index of the distribution.
This geometric mean makes sure that each value dispite its scale, equally contributes to the mean value. See more at Geometric Mean | What's the difference between Normalization and Standardization? | To solve the GPA/ACT or train/car problem, why not use the Geometric Mean?
n√(a1 × a2 × ... × an)
Where a* is the value from the distribution and n is the index of the distribution.
This geometric me | What's the difference between Normalization and Standardization?
To solve the GPA/ACT or train/car problem, why not use the Geometric Mean?
n√(a1 × a2 × ... × an)
Where a* is the value from the distribution and n is the index of the distribution.
This geometric mean makes sure that each value dispite its scale, equall... | What's the difference between Normalization and Standardization?
To solve the GPA/ACT or train/car problem, why not use the Geometric Mean?
n√(a1 × a2 × ... × an)
Where a* is the value from the distribution and n is the index of the distribution.
This geometric me |
891 | What's the difference between Normalization and Standardization? | In my field, data science, normalization is a transformation of data which allows easy comparison of the data downstream. There are many types of normalizations. Scaling being one of them. You can also log the data, or do anything else you want. The type of normalisation you use would depend on the outcome you want, si... | What's the difference between Normalization and Standardization? | In my field, data science, normalization is a transformation of data which allows easy comparison of the data downstream. There are many types of normalizations. Scaling being one of them. You can als | What's the difference between Normalization and Standardization?
In my field, data science, normalization is a transformation of data which allows easy comparison of the data downstream. There are many types of normalizations. Scaling being one of them. You can also log the data, or do anything else you want. The type ... | What's the difference between Normalization and Standardization?
In my field, data science, normalization is a transformation of data which allows easy comparison of the data downstream. There are many types of normalizations. Scaling being one of them. You can als |
892 | Does Julia have any hope of sticking in the statistical community? | I think the key will be whether or not libraries start being developed for Julia. It's all well and good to see toy examples (even if they are complicated toys) showing that Julia blows R out of the water at tasks R is bad at.
But poorly done loops and hand coded algorithms are not why many of the people I know who use... | Does Julia have any hope of sticking in the statistical community? | I think the key will be whether or not libraries start being developed for Julia. It's all well and good to see toy examples (even if they are complicated toys) showing that Julia blows R out of the w | Does Julia have any hope of sticking in the statistical community?
I think the key will be whether or not libraries start being developed for Julia. It's all well and good to see toy examples (even if they are complicated toys) showing that Julia blows R out of the water at tasks R is bad at.
But poorly done loops and ... | Does Julia have any hope of sticking in the statistical community?
I think the key will be whether or not libraries start being developed for Julia. It's all well and good to see toy examples (even if they are complicated toys) showing that Julia blows R out of the w |
893 | Does Julia have any hope of sticking in the statistical community? | I agree with a lot of the other comments. "Hope"? Sure. I think Julia has learned a lot from what R and Python/NumPy/Pandas and other systems have done right and wrong over the years. If I were smarter than I am, and wanted to write a new programming language that would be the substrate for a statistical development en... | Does Julia have any hope of sticking in the statistical community? | I agree with a lot of the other comments. "Hope"? Sure. I think Julia has learned a lot from what R and Python/NumPy/Pandas and other systems have done right and wrong over the years. If I were smarte | Does Julia have any hope of sticking in the statistical community?
I agree with a lot of the other comments. "Hope"? Sure. I think Julia has learned a lot from what R and Python/NumPy/Pandas and other systems have done right and wrong over the years. If I were smarter than I am, and wanted to write a new programming la... | Does Julia have any hope of sticking in the statistical community?
I agree with a lot of the other comments. "Hope"? Sure. I think Julia has learned a lot from what R and Python/NumPy/Pandas and other systems have done right and wrong over the years. If I were smarte |
894 | Does Julia have any hope of sticking in the statistical community? | For me, one very important thing for a data analysis language is to have query/relational algebra functionality with reasonable defaults and interactively-oriented design, and ideally this should be a built-in of the language. IMO, no FOSS language that I've used does this effectively, not even R.
data.frame is very cl... | Does Julia have any hope of sticking in the statistical community? | For me, one very important thing for a data analysis language is to have query/relational algebra functionality with reasonable defaults and interactively-oriented design, and ideally this should be a | Does Julia have any hope of sticking in the statistical community?
For me, one very important thing for a data analysis language is to have query/relational algebra functionality with reasonable defaults and interactively-oriented design, and ideally this should be a built-in of the language. IMO, no FOSS language that... | Does Julia have any hope of sticking in the statistical community?
For me, one very important thing for a data analysis language is to have query/relational algebra functionality with reasonable defaults and interactively-oriented design, and ideally this should be a |
895 | Does Julia have any hope of sticking in the statistical community? | I can sign under what Dirk and EpiGrad said; yet there is one more thing that makes R an unique lang in its niche -- data-oriented type system.
R's was especially designed for handling data, that's why it is vector-centered and has stuff like data.frames, factors, NAs and attributes.
Julia's types are on the other han... | Does Julia have any hope of sticking in the statistical community? | I can sign under what Dirk and EpiGrad said; yet there is one more thing that makes R an unique lang in its niche -- data-oriented type system.
R's was especially designed for handling data, that's w | Does Julia have any hope of sticking in the statistical community?
I can sign under what Dirk and EpiGrad said; yet there is one more thing that makes R an unique lang in its niche -- data-oriented type system.
R's was especially designed for handling data, that's why it is vector-centered and has stuff like data.fram... | Does Julia have any hope of sticking in the statistical community?
I can sign under what Dirk and EpiGrad said; yet there is one more thing that makes R an unique lang in its niche -- data-oriented type system.
R's was especially designed for handling data, that's w |
896 | Does Julia have any hope of sticking in the statistical community? | I can see Julia replacing Matlab, which would be a huge service for humanity.
To replace R, you'd need to consider all of the things that Neil G, Harlan, and others have mentioned, plus one big factor that I don't believe has been addressed: easy installation of the application and its libraries.
Right now, you can dow... | Does Julia have any hope of sticking in the statistical community? | I can see Julia replacing Matlab, which would be a huge service for humanity.
To replace R, you'd need to consider all of the things that Neil G, Harlan, and others have mentioned, plus one big factor | Does Julia have any hope of sticking in the statistical community?
I can see Julia replacing Matlab, which would be a huge service for humanity.
To replace R, you'd need to consider all of the things that Neil G, Harlan, and others have mentioned, plus one big factor that I don't believe has been addressed: easy instal... | Does Julia have any hope of sticking in the statistical community?
I can see Julia replacing Matlab, which would be a huge service for humanity.
To replace R, you'd need to consider all of the things that Neil G, Harlan, and others have mentioned, plus one big factor |
897 | Does Julia have any hope of sticking in the statistical community? | The Julia language is pretty new; it's time in the spot light can be measured in weeks (even though its development time can of course be measured in years). Now those weeks in the spot light were very exciting weeks---see for example the recent talk at Stanford where "it had just started"---but what you ask for in ter... | Does Julia have any hope of sticking in the statistical community? | The Julia language is pretty new; it's time in the spot light can be measured in weeks (even though its development time can of course be measured in years). Now those weeks in the spot light were ver | Does Julia have any hope of sticking in the statistical community?
The Julia language is pretty new; it's time in the spot light can be measured in weeks (even though its development time can of course be measured in years). Now those weeks in the spot light were very exciting weeks---see for example the recent talk at... | Does Julia have any hope of sticking in the statistical community?
The Julia language is pretty new; it's time in the spot light can be measured in weeks (even though its development time can of course be measured in years). Now those weeks in the spot light were ver |
898 | Does Julia have any hope of sticking in the statistical community? | Bruce Tate here, author of Seven Languages in Seven Weeks. Here are a few thoughts. I am working on Julia for the followup book. The following is just my opinion after a few weeks of play.
There are two fundamental forces at play. First, all languages have a lifespan. R will be replaced some day. We don't know when. N... | Does Julia have any hope of sticking in the statistical community? | Bruce Tate here, author of Seven Languages in Seven Weeks. Here are a few thoughts. I am working on Julia for the followup book. The following is just my opinion after a few weeks of play.
There are | Does Julia have any hope of sticking in the statistical community?
Bruce Tate here, author of Seven Languages in Seven Weeks. Here are a few thoughts. I am working on Julia for the followup book. The following is just my opinion after a few weeks of play.
There are two fundamental forces at play. First, all languages ... | Does Julia have any hope of sticking in the statistical community?
Bruce Tate here, author of Seven Languages in Seven Weeks. Here are a few thoughts. I am working on Julia for the followup book. The following is just my opinion after a few weeks of play.
There are |
899 | Does Julia have any hope of sticking in the statistical community? | Every time I see a new language, I ask myself why an existing language can't be improved instead.
Python's big advantages are
a rich set of modules (not just statistics, but plotting libraries, output to pdf, etc.)
language constructs that you end up needing in the long run (objected-oriented constructs you need in a ... | Does Julia have any hope of sticking in the statistical community? | Every time I see a new language, I ask myself why an existing language can't be improved instead.
Python's big advantages are
a rich set of modules (not just statistics, but plotting libraries, outpu | Does Julia have any hope of sticking in the statistical community?
Every time I see a new language, I ask myself why an existing language can't be improved instead.
Python's big advantages are
a rich set of modules (not just statistics, but plotting libraries, output to pdf, etc.)
language constructs that you end up n... | Does Julia have any hope of sticking in the statistical community?
Every time I see a new language, I ask myself why an existing language can't be improved instead.
Python's big advantages are
a rich set of modules (not just statistics, but plotting libraries, outpu |
900 | Does Julia have any hope of sticking in the statistical community? | Julia will not take over R very soon. Check out Microsoft R open.
https://mran.revolutionanalytics.com/open/
This is an enhanced version of R that automatically uses all the cores of your computer. It is the same R, same language, same packages. When you install it, RStudio will also use it in the console. The speed of... | Does Julia have any hope of sticking in the statistical community? | Julia will not take over R very soon. Check out Microsoft R open.
https://mran.revolutionanalytics.com/open/
This is an enhanced version of R that automatically uses all the cores of your computer. It | Does Julia have any hope of sticking in the statistical community?
Julia will not take over R very soon. Check out Microsoft R open.
https://mran.revolutionanalytics.com/open/
This is an enhanced version of R that automatically uses all the cores of your computer. It is the same R, same language, same packages. When yo... | Does Julia have any hope of sticking in the statistical community?
Julia will not take over R very soon. Check out Microsoft R open.
https://mran.revolutionanalytics.com/open/
This is an enhanced version of R that automatically uses all the cores of your computer. It |
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