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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9,601 | Are gridlines and grey backgrounds chartjunk and should they be used only on an exception basis? | According to Kaye Basford & John Tukey in Graphical Analysis of Multi-Response Data, p. 373,
The purpose of plotting is to convey phenomena to the viewer's cortex, not to provide a place to lookup observed numbers.
By that measure, grid lines are best avoided. Further, the gray background of ggplot2 reduces the cont... | Are gridlines and grey backgrounds chartjunk and should they be used only on an exception basis? | According to Kaye Basford & John Tukey in Graphical Analysis of Multi-Response Data, p. 373,
The purpose of plotting is to convey phenomena to the viewer's cortex, not to provide a place to lookup o | Are gridlines and grey backgrounds chartjunk and should they be used only on an exception basis?
According to Kaye Basford & John Tukey in Graphical Analysis of Multi-Response Data, p. 373,
The purpose of plotting is to convey phenomena to the viewer's cortex, not to provide a place to lookup observed numbers.
By th... | Are gridlines and grey backgrounds chartjunk and should they be used only on an exception basis?
According to Kaye Basford & John Tukey in Graphical Analysis of Multi-Response Data, p. 373,
The purpose of plotting is to convey phenomena to the viewer's cortex, not to provide a place to lookup o |
9,602 | Are gridlines and grey backgrounds chartjunk and should they be used only on an exception basis? | Personally I prefer the dark-on-light theme_bw, or even theme_minimal, to ggplot2's default theme -- see http://docs.ggplot2.org/current/ggtheme.html.
I find the grey background in the default theme extremely distracting -- my eye is drawn to the grey blocks rather than the points. Does that happen to anyone else? F... | Are gridlines and grey backgrounds chartjunk and should they be used only on an exception basis? | Personally I prefer the dark-on-light theme_bw, or even theme_minimal, to ggplot2's default theme -- see http://docs.ggplot2.org/current/ggtheme.html.
I find the grey background in the default theme | Are gridlines and grey backgrounds chartjunk and should they be used only on an exception basis?
Personally I prefer the dark-on-light theme_bw, or even theme_minimal, to ggplot2's default theme -- see http://docs.ggplot2.org/current/ggtheme.html.
I find the grey background in the default theme extremely distracting -... | Are gridlines and grey backgrounds chartjunk and should they be used only on an exception basis?
Personally I prefer the dark-on-light theme_bw, or even theme_minimal, to ggplot2's default theme -- see http://docs.ggplot2.org/current/ggtheme.html.
I find the grey background in the default theme |
9,603 | Is it common practice to minimize the mean loss over the batches instead of the sum? | As mentioned by pkubik, usually there's a regularization term for the parameters that doesn't depend on the input, for instance in tensorflow it's like
# Loss function using L2 Regularization
regularizer = tf.nn.l2_loss(weights)
loss = tf.reduce_mean(loss + beta * regularizer)
In this case averaging over the mini-batc... | Is it common practice to minimize the mean loss over the batches instead of the sum? | As mentioned by pkubik, usually there's a regularization term for the parameters that doesn't depend on the input, for instance in tensorflow it's like
# Loss function using L2 Regularization
regulari | Is it common practice to minimize the mean loss over the batches instead of the sum?
As mentioned by pkubik, usually there's a regularization term for the parameters that doesn't depend on the input, for instance in tensorflow it's like
# Loss function using L2 Regularization
regularizer = tf.nn.l2_loss(weights)
loss =... | Is it common practice to minimize the mean loss over the batches instead of the sum?
As mentioned by pkubik, usually there's a regularization term for the parameters that doesn't depend on the input, for instance in tensorflow it's like
# Loss function using L2 Regularization
regulari |
9,604 | Is it common practice to minimize the mean loss over the batches instead of the sum? | I will focus on the part:
I don't understand how this would affect the backprop calculation.
First of all you've probably already noticed that the only difference between the resulting loss values is that the average loss is scaled down with respect to the sum by the factor of $\frac{1}{B}$, i.e. that $L_{SUM} = B \c... | Is it common practice to minimize the mean loss over the batches instead of the sum? | I will focus on the part:
I don't understand how this would affect the backprop calculation.
First of all you've probably already noticed that the only difference between the resulting loss values i | Is it common practice to minimize the mean loss over the batches instead of the sum?
I will focus on the part:
I don't understand how this would affect the backprop calculation.
First of all you've probably already noticed that the only difference between the resulting loss values is that the average loss is scaled d... | Is it common practice to minimize the mean loss over the batches instead of the sum?
I will focus on the part:
I don't understand how this would affect the backprop calculation.
First of all you've probably already noticed that the only difference between the resulting loss values i |
9,605 | How the 'NA' values are treated in glm in R | NA Handling: You can control how glm handles missing data. glm() has an argument na.action which indicates which of the following generic functions should be used by glm to handle NA in the data:
na.omit and na.exclude: observations are removed if they contain any missing values; if na.exclude is used some functions... | How the 'NA' values are treated in glm in R | NA Handling: You can control how glm handles missing data. glm() has an argument na.action which indicates which of the following generic functions should be used by glm to handle NA in the data:
na | How the 'NA' values are treated in glm in R
NA Handling: You can control how glm handles missing data. glm() has an argument na.action which indicates which of the following generic functions should be used by glm to handle NA in the data:
na.omit and na.exclude: observations are removed if they contain any missing v... | How the 'NA' values are treated in glm in R
NA Handling: You can control how glm handles missing data. glm() has an argument na.action which indicates which of the following generic functions should be used by glm to handle NA in the data:
na |
9,606 | Why stock prices are lognormal but stock returns are normal | Some points to start with:
i) these distributional conventions are at best approximations. They can be convenient models, but we shouldn't confuse that with the actual distribution of stock prices or returns.
ii) stock prices are typically increasing (but in any case, have changing mean; the mean isn't stable). So when... | Why stock prices are lognormal but stock returns are normal | Some points to start with:
i) these distributional conventions are at best approximations. They can be convenient models, but we shouldn't confuse that with the actual distribution of stock prices or | Why stock prices are lognormal but stock returns are normal
Some points to start with:
i) these distributional conventions are at best approximations. They can be convenient models, but we shouldn't confuse that with the actual distribution of stock prices or returns.
ii) stock prices are typically increasing (but in a... | Why stock prices are lognormal but stock returns are normal
Some points to start with:
i) these distributional conventions are at best approximations. They can be convenient models, but we shouldn't confuse that with the actual distribution of stock prices or |
9,607 | A good Gibbs sampling tutorials and references | I'd start with:
Casella, George; George, Edward I. (1992). "Explaining the Gibbs sampler". The American Statistician 46 (3): 167–174. (FREE PDF)
Abstract: Computer-intensive algorithms, such as the Gibbs sampler, have become increasingly popular statistical tools, both in applied and theoretical work. The properties o... | A good Gibbs sampling tutorials and references | I'd start with:
Casella, George; George, Edward I. (1992). "Explaining the Gibbs sampler". The American Statistician 46 (3): 167–174. (FREE PDF)
Abstract: Computer-intensive algorithms, such as the G | A good Gibbs sampling tutorials and references
I'd start with:
Casella, George; George, Edward I. (1992). "Explaining the Gibbs sampler". The American Statistician 46 (3): 167–174. (FREE PDF)
Abstract: Computer-intensive algorithms, such as the Gibbs sampler, have become increasingly popular statistical tools, both in... | A good Gibbs sampling tutorials and references
I'd start with:
Casella, George; George, Edward I. (1992). "Explaining the Gibbs sampler". The American Statistician 46 (3): 167–174. (FREE PDF)
Abstract: Computer-intensive algorithms, such as the G |
9,608 | A good Gibbs sampling tutorials and references | One online article that really helped me understand Gibbs Sampling is Parameter estimation for text analysis by Gregor Heinrich. It's not a general Gibbs sampling tutorial but it discusses it in terms of latent dirichlet allocation, a fairly popular Bayesian model for document modeling. It goes into the math in fair de... | A good Gibbs sampling tutorials and references | One online article that really helped me understand Gibbs Sampling is Parameter estimation for text analysis by Gregor Heinrich. It's not a general Gibbs sampling tutorial but it discusses it in terms | A good Gibbs sampling tutorials and references
One online article that really helped me understand Gibbs Sampling is Parameter estimation for text analysis by Gregor Heinrich. It's not a general Gibbs sampling tutorial but it discusses it in terms of latent dirichlet allocation, a fairly popular Bayesian model for docu... | A good Gibbs sampling tutorials and references
One online article that really helped me understand Gibbs Sampling is Parameter estimation for text analysis by Gregor Heinrich. It's not a general Gibbs sampling tutorial but it discusses it in terms |
9,609 | A good Gibbs sampling tutorials and references | The book Monte Carlo Strategies in Scientific Computing is an excellent resource. It does address things in a mathematically rigorous way, but you can easily skip mathematical sections that don't interest you and still get tons of practical advice out of it. In particular, it does a nice job of tying together Metropoli... | A good Gibbs sampling tutorials and references | The book Monte Carlo Strategies in Scientific Computing is an excellent resource. It does address things in a mathematically rigorous way, but you can easily skip mathematical sections that don't inte | A good Gibbs sampling tutorials and references
The book Monte Carlo Strategies in Scientific Computing is an excellent resource. It does address things in a mathematically rigorous way, but you can easily skip mathematical sections that don't interest you and still get tons of practical advice out of it. In particular,... | A good Gibbs sampling tutorials and references
The book Monte Carlo Strategies in Scientific Computing is an excellent resource. It does address things in a mathematically rigorous way, but you can easily skip mathematical sections that don't inte |
9,610 | Why second order SGD convergence methods are unpopular for deep learning? | Should we go toward second order methods for deep learning?
TL;DR: No, especially now when the pace of innovation is slowing down, and we're seeing less new architectural innovations, and more ways to train what are basically just copies of existing architectures, on larger datasets (see OpenAI's GPT-2).
First, withou... | Why second order SGD convergence methods are unpopular for deep learning? | Should we go toward second order methods for deep learning?
TL;DR: No, especially now when the pace of innovation is slowing down, and we're seeing less new architectural innovations, and more ways t | Why second order SGD convergence methods are unpopular for deep learning?
Should we go toward second order methods for deep learning?
TL;DR: No, especially now when the pace of innovation is slowing down, and we're seeing less new architectural innovations, and more ways to train what are basically just copies of exis... | Why second order SGD convergence methods are unpopular for deep learning?
Should we go toward second order methods for deep learning?
TL;DR: No, especially now when the pace of innovation is slowing down, and we're seeing less new architectural innovations, and more ways t |
9,611 | Why second order SGD convergence methods are unpopular for deep learning? | This is actually starting to change as recent work are showing the benefit of second order methods specially for NLP problems. Some examples are:
"ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning"
Zhewei Yao, Amir Gholami, Sheng Shen, Kurt Keutzer, Michael W. Mahoney
"Second Order Optimization Made... | Why second order SGD convergence methods are unpopular for deep learning? | This is actually starting to change as recent work are showing the benefit of second order methods specially for NLP problems. Some examples are:
"ADAHESSIAN: An Adaptive Second Order Optimizer for M | Why second order SGD convergence methods are unpopular for deep learning?
This is actually starting to change as recent work are showing the benefit of second order methods specially for NLP problems. Some examples are:
"ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning"
Zhewei Yao, Amir Gholami, She... | Why second order SGD convergence methods are unpopular for deep learning?
This is actually starting to change as recent work are showing the benefit of second order methods specially for NLP problems. Some examples are:
"ADAHESSIAN: An Adaptive Second Order Optimizer for M |
9,612 | Converting similarity matrix to (euclidean) distance matrix | According to cosine theorem, in euclidean space the (euclidean) squared distance between two points (vectors) 1 and 2 is $d_{12}^2 = h_1^2+h_2^2-2h_1h_2\cos\phi$. Squared lengths $h_1^2$ and $h_2^2$ are the sums of squared coordinates of points 1 and 2, respectively (they are the pythagorean hypotenuses). Quantity $h_1... | Converting similarity matrix to (euclidean) distance matrix | According to cosine theorem, in euclidean space the (euclidean) squared distance between two points (vectors) 1 and 2 is $d_{12}^2 = h_1^2+h_2^2-2h_1h_2\cos\phi$. Squared lengths $h_1^2$ and $h_2^2$ a | Converting similarity matrix to (euclidean) distance matrix
According to cosine theorem, in euclidean space the (euclidean) squared distance between two points (vectors) 1 and 2 is $d_{12}^2 = h_1^2+h_2^2-2h_1h_2\cos\phi$. Squared lengths $h_1^2$ and $h_2^2$ are the sums of squared coordinates of points 1 and 2, respec... | Converting similarity matrix to (euclidean) distance matrix
According to cosine theorem, in euclidean space the (euclidean) squared distance between two points (vectors) 1 and 2 is $d_{12}^2 = h_1^2+h_2^2-2h_1h_2\cos\phi$. Squared lengths $h_1^2$ and $h_2^2$ a |
9,613 | Is Student's t test a Wald test? | As Wasserman defines the Wald test, the statistic used in the t-test is certainly the Wald-statistic defined there:
$$W=\frac{\hat{\theta}-\theta_0}{\hat{\text{se}}(\hat{\theta})}$$
However, the Wald test uses an asymptotic argument to compare that statistic with a standard normal distribution. [The Wald test when deal... | Is Student's t test a Wald test? | As Wasserman defines the Wald test, the statistic used in the t-test is certainly the Wald-statistic defined there:
$$W=\frac{\hat{\theta}-\theta_0}{\hat{\text{se}}(\hat{\theta})}$$
However, the Wald | Is Student's t test a Wald test?
As Wasserman defines the Wald test, the statistic used in the t-test is certainly the Wald-statistic defined there:
$$W=\frac{\hat{\theta}-\theta_0}{\hat{\text{se}}(\hat{\theta})}$$
However, the Wald test uses an asymptotic argument to compare that statistic with a standard normal distr... | Is Student's t test a Wald test?
As Wasserman defines the Wald test, the statistic used in the t-test is certainly the Wald-statistic defined there:
$$W=\frac{\hat{\theta}-\theta_0}{\hat{\text{se}}(\hat{\theta})}$$
However, the Wald |
9,614 | Is Student's t test a Wald test? | @Glen_b has provided an excellent answer to the topic. I want to add that, in the t-test, the distribution is the t-distribution. For example, you'd need to know the degree of freedom for your statistics. However, the wald-test relies on the chi-square distribution (square of standard normal). Of course, as the degree ... | Is Student's t test a Wald test? | @Glen_b has provided an excellent answer to the topic. I want to add that, in the t-test, the distribution is the t-distribution. For example, you'd need to know the degree of freedom for your statist | Is Student's t test a Wald test?
@Glen_b has provided an excellent answer to the topic. I want to add that, in the t-test, the distribution is the t-distribution. For example, you'd need to know the degree of freedom for your statistics. However, the wald-test relies on the chi-square distribution (square of standard n... | Is Student's t test a Wald test?
@Glen_b has provided an excellent answer to the topic. I want to add that, in the t-test, the distribution is the t-distribution. For example, you'd need to know the degree of freedom for your statist |
9,615 | Problem with proof of Conditional expectation as best predictor | (This is an adaptation from Granger & Newbold(1986) "Forecasting Economic Time Series").
By construction, your error cost function is $\left[Y-g(X)\right]^2$. This incorporates a critical assumption (that the error cost function is symmetric around zero) -a different error cost function would not necessarily have the ... | Problem with proof of Conditional expectation as best predictor | (This is an adaptation from Granger & Newbold(1986) "Forecasting Economic Time Series").
By construction, your error cost function is $\left[Y-g(X)\right]^2$. This incorporates a critical assumption | Problem with proof of Conditional expectation as best predictor
(This is an adaptation from Granger & Newbold(1986) "Forecasting Economic Time Series").
By construction, your error cost function is $\left[Y-g(X)\right]^2$. This incorporates a critical assumption (that the error cost function is symmetric around zero) ... | Problem with proof of Conditional expectation as best predictor
(This is an adaptation from Granger & Newbold(1986) "Forecasting Economic Time Series").
By construction, your error cost function is $\left[Y-g(X)\right]^2$. This incorporates a critical assumption |
9,616 | Problem with proof of Conditional expectation as best predictor | There's a mathematical point of view that is very simple. What you have is a projection problem in a Hilbert space, much like projecting a vector in $\mathbb{R}^n$ onto a subspace.
Let $(\Omega, \mathcal{F}, \mu)$ denote the underlying probability space. For the problem to make sense, consider the random variables with... | Problem with proof of Conditional expectation as best predictor | There's a mathematical point of view that is very simple. What you have is a projection problem in a Hilbert space, much like projecting a vector in $\mathbb{R}^n$ onto a subspace.
Let $(\Omega, \math | Problem with proof of Conditional expectation as best predictor
There's a mathematical point of view that is very simple. What you have is a projection problem in a Hilbert space, much like projecting a vector in $\mathbb{R}^n$ onto a subspace.
Let $(\Omega, \mathcal{F}, \mu)$ denote the underlying probability space. F... | Problem with proof of Conditional expectation as best predictor
There's a mathematical point of view that is very simple. What you have is a projection problem in a Hilbert space, much like projecting a vector in $\mathbb{R}^n$ onto a subspace.
Let $(\Omega, \math |
9,617 | Problem with proof of Conditional expectation as best predictor | Note that to prove the answer, you really only need to show that
$$E \Big[ -2 \big(Y - E(Y|X)\big) \big(E(Y|X) - g(X)\big) \Big] = 0$$
As for which expectation to take, you take it conditionally, otherwise the term
$$\arg \min_{g(X)} E\Big[\big(Y - g(X)\big)^2\Big]$$
Doesn't make sense, as $g(X)$ is a random variable ... | Problem with proof of Conditional expectation as best predictor | Note that to prove the answer, you really only need to show that
$$E \Big[ -2 \big(Y - E(Y|X)\big) \big(E(Y|X) - g(X)\big) \Big] = 0$$
As for which expectation to take, you take it conditionally, oth | Problem with proof of Conditional expectation as best predictor
Note that to prove the answer, you really only need to show that
$$E \Big[ -2 \big(Y - E(Y|X)\big) \big(E(Y|X) - g(X)\big) \Big] = 0$$
As for which expectation to take, you take it conditionally, otherwise the term
$$\arg \min_{g(X)} E\Big[\big(Y - g(X)\b... | Problem with proof of Conditional expectation as best predictor
Note that to prove the answer, you really only need to show that
$$E \Big[ -2 \big(Y - E(Y|X)\big) \big(E(Y|X) - g(X)\big) \Big] = 0$$
As for which expectation to take, you take it conditionally, oth |
9,618 | Problem with proof of Conditional expectation as best predictor | Regarding your last question, the expectation can be either w.r.t. $p(x,y)$ (the unconditional error) or w.r.t. $p(y\mid x)$ (the conditional error at each value $X = x$). Happily, minimizing the conditional error at each value $X = x$ also minimizes the unconditional error, so this is not a crucial distinction. | Problem with proof of Conditional expectation as best predictor | Regarding your last question, the expectation can be either w.r.t. $p(x,y)$ (the unconditional error) or w.r.t. $p(y\mid x)$ (the conditional error at each value $X = x$). Happily, minimizing the cond | Problem with proof of Conditional expectation as best predictor
Regarding your last question, the expectation can be either w.r.t. $p(x,y)$ (the unconditional error) or w.r.t. $p(y\mid x)$ (the conditional error at each value $X = x$). Happily, minimizing the conditional error at each value $X = x$ also minimizes the u... | Problem with proof of Conditional expectation as best predictor
Regarding your last question, the expectation can be either w.r.t. $p(x,y)$ (the unconditional error) or w.r.t. $p(y\mid x)$ (the conditional error at each value $X = x$). Happily, minimizing the cond |
9,619 | Self study vs a taught education? | I think I'm in a fairly similar place, but I'll take a stab. I started out as a sociology graduate student and, once I had completed all of the stats courses available through my department, wandered into some grad-level courses from the stats department at my university. It was a revelation; the way that the stats p... | Self study vs a taught education? | I think I'm in a fairly similar place, but I'll take a stab. I started out as a sociology graduate student and, once I had completed all of the stats courses available through my department, wandered | Self study vs a taught education?
I think I'm in a fairly similar place, but I'll take a stab. I started out as a sociology graduate student and, once I had completed all of the stats courses available through my department, wandered into some grad-level courses from the stats department at my university. It was a re... | Self study vs a taught education?
I think I'm in a fairly similar place, but I'll take a stab. I started out as a sociology graduate student and, once I had completed all of the stats courses available through my department, wandered |
9,620 | Self study vs a taught education? | +1 for a great question. I think in the long run you are just always going to have to rely on self-study in one form or another. If you feel uncomfortable with the fundamentals, formal classes will be great. For example, if you feel solid on applied stats, but don't feel like you have an understanding of the underly... | Self study vs a taught education? | +1 for a great question. I think in the long run you are just always going to have to rely on self-study in one form or another. If you feel uncomfortable with the fundamentals, formal classes will | Self study vs a taught education?
+1 for a great question. I think in the long run you are just always going to have to rely on self-study in one form or another. If you feel uncomfortable with the fundamentals, formal classes will be great. For example, if you feel solid on applied stats, but don't feel like you ha... | Self study vs a taught education?
+1 for a great question. I think in the long run you are just always going to have to rely on self-study in one form or another. If you feel uncomfortable with the fundamentals, formal classes will |
9,621 | Self study vs a taught education? | The theoretical basis of statistics is too deep to be able to get a good understanding of the subject just from working on the problems that happen to fall on your desk. Some of the biggest statistical prat-falls I've seen have been from people with programming or mathematical backgrounds who blithely assumed that kno... | Self study vs a taught education? | The theoretical basis of statistics is too deep to be able to get a good understanding of the subject just from working on the problems that happen to fall on your desk. Some of the biggest statistic | Self study vs a taught education?
The theoretical basis of statistics is too deep to be able to get a good understanding of the subject just from working on the problems that happen to fall on your desk. Some of the biggest statistical prat-falls I've seen have been from people with programming or mathematical backgro... | Self study vs a taught education?
The theoretical basis of statistics is too deep to be able to get a good understanding of the subject just from working on the problems that happen to fall on your desk. Some of the biggest statistic |
9,622 | Self study vs a taught education? | For programming I agree that self-study is the way to go. I taught myself R over a period of a few months as I work as a statistician. I then took a Coursera course in R programming to see if I could learn anything new, and as I had a solid background I aced it and was invited to be teaching assistant on the course.
... | Self study vs a taught education? | For programming I agree that self-study is the way to go. I taught myself R over a period of a few months as I work as a statistician. I then took a Coursera course in R programming to see if I coul | Self study vs a taught education?
For programming I agree that self-study is the way to go. I taught myself R over a period of a few months as I work as a statistician. I then took a Coursera course in R programming to see if I could learn anything new, and as I had a solid background I aced it and was invited to be ... | Self study vs a taught education?
For programming I agree that self-study is the way to go. I taught myself R over a period of a few months as I work as a statistician. I then took a Coursera course in R programming to see if I coul |
9,623 | Loss function for autoencoders | I think the best answer to this is that the cross-entropy loss function is just not well-suited to this particular task.
In taking this approach, you are essentially saying the true MNIST data is binary, and your pixel intensities represent the probability that each pixel is 'on.' But we know this is not actually the c... | Loss function for autoencoders | I think the best answer to this is that the cross-entropy loss function is just not well-suited to this particular task.
In taking this approach, you are essentially saying the true MNIST data is bina | Loss function for autoencoders
I think the best answer to this is that the cross-entropy loss function is just not well-suited to this particular task.
In taking this approach, you are essentially saying the true MNIST data is binary, and your pixel intensities represent the probability that each pixel is 'on.' But we ... | Loss function for autoencoders
I think the best answer to this is that the cross-entropy loss function is just not well-suited to this particular task.
In taking this approach, you are essentially saying the true MNIST data is bina |
9,624 | Loss function for autoencoders | I went through an autoencoder example listed at https://colab.research.google.com/github/ageron/handson-ml2/blob/master/17_autoencoders_and_gans.ipynb. The author used the binary cross-entropy loss function, and it seemed to work fine. I replaced it with the mse loss function, and the results were similar. Note that th... | Loss function for autoencoders | I went through an autoencoder example listed at https://colab.research.google.com/github/ageron/handson-ml2/blob/master/17_autoencoders_and_gans.ipynb. The author used the binary cross-entropy loss fu | Loss function for autoencoders
I went through an autoencoder example listed at https://colab.research.google.com/github/ageron/handson-ml2/blob/master/17_autoencoders_and_gans.ipynb. The author used the binary cross-entropy loss function, and it seemed to work fine. I replaced it with the mse loss function, and the res... | Loss function for autoencoders
I went through an autoencoder example listed at https://colab.research.google.com/github/ageron/handson-ml2/blob/master/17_autoencoders_and_gans.ipynb. The author used the binary cross-entropy loss fu |
9,625 | Multilabel classification metrics on scikit | The subset accuracy is indeed a harsh metric. To get a sense of how good or bad 0.29 is, some idea:
look at how many labels you have an average for each sample
look at the inter-annotator agreement, if available (if not, try yourself to see what subset accuracy the obtained when you are the classifier)
think whether t... | Multilabel classification metrics on scikit | The subset accuracy is indeed a harsh metric. To get a sense of how good or bad 0.29 is, some idea:
look at how many labels you have an average for each sample
look at the inter-annotator agreement, | Multilabel classification metrics on scikit
The subset accuracy is indeed a harsh metric. To get a sense of how good or bad 0.29 is, some idea:
look at how many labels you have an average for each sample
look at the inter-annotator agreement, if available (if not, try yourself to see what subset accuracy the obtained ... | Multilabel classification metrics on scikit
The subset accuracy is indeed a harsh metric. To get a sense of how good or bad 0.29 is, some idea:
look at how many labels you have an average for each sample
look at the inter-annotator agreement, |
9,626 | Multilabel classification metrics on scikit | The Hamming-Loss and Exact match (also called subset accuracy) can be calculated Using Scikit-learn as follows.
import numpy as np
from sklearn.metrics import hamming_loss, accuracy_score
y_true = np.array([[0,1,0],
[0,1,1],
[1,0,1],
[0,0,1]])
y_pred = np.array... | Multilabel classification metrics on scikit | The Hamming-Loss and Exact match (also called subset accuracy) can be calculated Using Scikit-learn as follows.
import numpy as np
from sklearn.metrics import hamming_loss, accuracy_score
y_true = np | Multilabel classification metrics on scikit
The Hamming-Loss and Exact match (also called subset accuracy) can be calculated Using Scikit-learn as follows.
import numpy as np
from sklearn.metrics import hamming_loss, accuracy_score
y_true = np.array([[0,1,0],
[0,1,1],
[1,0,1],
... | Multilabel classification metrics on scikit
The Hamming-Loss and Exact match (also called subset accuracy) can be calculated Using Scikit-learn as follows.
import numpy as np
from sklearn.metrics import hamming_loss, accuracy_score
y_true = np |
9,627 | Multilabel classification metrics on scikit | Is the 0.29 score not enough? What does your confusion matrix look like? Are there some topics that cannot be separated out maybe by only looking at the word contents?
Otherwise, try to turn your problem around: Hypothesise that the low scores is actually the best your classifier can do on your data. That would mean th... | Multilabel classification metrics on scikit | Is the 0.29 score not enough? What does your confusion matrix look like? Are there some topics that cannot be separated out maybe by only looking at the word contents?
Otherwise, try to turn your prob | Multilabel classification metrics on scikit
Is the 0.29 score not enough? What does your confusion matrix look like? Are there some topics that cannot be separated out maybe by only looking at the word contents?
Otherwise, try to turn your problem around: Hypothesise that the low scores is actually the best your classi... | Multilabel classification metrics on scikit
Is the 0.29 score not enough? What does your confusion matrix look like? Are there some topics that cannot be separated out maybe by only looking at the word contents?
Otherwise, try to turn your prob |
9,628 | Multilabel classification metrics on scikit | The following is a vectorized version of the Hamming score:
import numpy as np
def hamming_score(pred, answer):
out = ((pred & answer).sum(axis=1) / (pred | answer).sum(axis=1)).mean()
if np.isinf(out):
out = np.array(1.0)
return out
pred = np.array([[0, 1, 1], [0, 1, 1], [0, 1, 0], [0, 0, 0]])
... | Multilabel classification metrics on scikit | The following is a vectorized version of the Hamming score:
import numpy as np
def hamming_score(pred, answer):
out = ((pred & answer).sum(axis=1) / (pred | answer).sum(axis=1)).mean()
if np | Multilabel classification metrics on scikit
The following is a vectorized version of the Hamming score:
import numpy as np
def hamming_score(pred, answer):
out = ((pred & answer).sum(axis=1) / (pred | answer).sum(axis=1)).mean()
if np.isinf(out):
out = np.array(1.0)
return out
pred = np.array([[... | Multilabel classification metrics on scikit
The following is a vectorized version of the Hamming score:
import numpy as np
def hamming_score(pred, answer):
out = ((pred & answer).sum(axis=1) / (pred | answer).sum(axis=1)).mean()
if np |
9,629 | Why PCA of data by means of SVD of the data? | Here are my 2ct on the topic
The chemometrics lecture where I first learned PCA used solution (2), but it was not numerically oriented, and my numerics lecture was only an introduction and didn't discuss SVD as far as I recall.
If I understand Holmes: Fast SVD for Large-Scale Matrices correctly, your idea has been us... | Why PCA of data by means of SVD of the data? | Here are my 2ct on the topic
The chemometrics lecture where I first learned PCA used solution (2), but it was not numerically oriented, and my numerics lecture was only an introduction and didn't dis | Why PCA of data by means of SVD of the data?
Here are my 2ct on the topic
The chemometrics lecture where I first learned PCA used solution (2), but it was not numerically oriented, and my numerics lecture was only an introduction and didn't discuss SVD as far as I recall.
If I understand Holmes: Fast SVD for Large-Sc... | Why PCA of data by means of SVD of the data?
Here are my 2ct on the topic
The chemometrics lecture where I first learned PCA used solution (2), but it was not numerically oriented, and my numerics lecture was only an introduction and didn't dis |
9,630 | Why PCA of data by means of SVD of the data? | SVD is slower but is often considered to be the preferred method because of its higher numerical accuracy.
As you state in the question, principal component analysis (PCA) can be carried out either by SVD of the centered data matrix $\mathbf X$ (see this Q&A thread for more details) or by the eigen-decomposition of the... | Why PCA of data by means of SVD of the data? | SVD is slower but is often considered to be the preferred method because of its higher numerical accuracy.
As you state in the question, principal component analysis (PCA) can be carried out either by | Why PCA of data by means of SVD of the data?
SVD is slower but is often considered to be the preferred method because of its higher numerical accuracy.
As you state in the question, principal component analysis (PCA) can be carried out either by SVD of the centered data matrix $\mathbf X$ (see this Q&A thread for more ... | Why PCA of data by means of SVD of the data?
SVD is slower but is often considered to be the preferred method because of its higher numerical accuracy.
As you state in the question, principal component analysis (PCA) can be carried out either by |
9,631 | How do Bayesians compare distributions? | Think your statement through as a Frequentist and make it more specific first. A Frequentist could not say that "data set A is different from data set B", without any further clarification.
First, you'd have to state what you mean by "different". Perhaps you mean "have different mean values". Then again, you might mean... | How do Bayesians compare distributions? | Think your statement through as a Frequentist and make it more specific first. A Frequentist could not say that "data set A is different from data set B", without any further clarification.
First, you | How do Bayesians compare distributions?
Think your statement through as a Frequentist and make it more specific first. A Frequentist could not say that "data set A is different from data set B", without any further clarification.
First, you'd have to state what you mean by "different". Perhaps you mean "have different ... | How do Bayesians compare distributions?
Think your statement through as a Frequentist and make it more specific first. A Frequentist could not say that "data set A is different from data set B", without any further clarification.
First, you |
9,632 | How do Bayesians compare distributions? | this paper might be of interest:
http://arxiv.org/pdf/0906.4032v1.pdf
It gives a nice summary of some frequentist and Bayesian approaches to the two sample problem, and discusses both the parametric and nonparametric cases.
It might add something to the other answers to give a simple example. Say you have two data set... | How do Bayesians compare distributions? | this paper might be of interest:
http://arxiv.org/pdf/0906.4032v1.pdf
It gives a nice summary of some frequentist and Bayesian approaches to the two sample problem, and discusses both the parametric a | How do Bayesians compare distributions?
this paper might be of interest:
http://arxiv.org/pdf/0906.4032v1.pdf
It gives a nice summary of some frequentist and Bayesian approaches to the two sample problem, and discusses both the parametric and nonparametric cases.
It might add something to the other answers to give a si... | How do Bayesians compare distributions?
this paper might be of interest:
http://arxiv.org/pdf/0906.4032v1.pdf
It gives a nice summary of some frequentist and Bayesian approaches to the two sample problem, and discusses both the parametric a |
9,633 | How do Bayesians compare distributions? | Given data, how strongly do we believe that 2 groups do not come from the same population (H_1: they do not come from the same population vs H_0: they come from the same population). This can be done with a Bayesian t-test.
Complexity is used to figure out how much the prior is overlapping with one hypothesis. Fit is ... | How do Bayesians compare distributions? | Given data, how strongly do we believe that 2 groups do not come from the same population (H_1: they do not come from the same population vs H_0: they come from the same population). This can be done | How do Bayesians compare distributions?
Given data, how strongly do we believe that 2 groups do not come from the same population (H_1: they do not come from the same population vs H_0: they come from the same population). This can be done with a Bayesian t-test.
Complexity is used to figure out how much the prior is ... | How do Bayesians compare distributions?
Given data, how strongly do we believe that 2 groups do not come from the same population (H_1: they do not come from the same population vs H_0: they come from the same population). This can be done |
9,634 | What causes the discontinuity in the distribution of published p-values at p < .05? | (1) As already mentioned by @PeterFlom, one explanation might be related to the "file drawer" problem. (2) @Zen also mentioned the case where the author(s) manipulate(s) the data or the models (e.g. data dredging). (3) However, we do not test hypotheses on a purely random basis. That is, hypotheses are not chosen by ch... | What causes the discontinuity in the distribution of published p-values at p < .05? | (1) As already mentioned by @PeterFlom, one explanation might be related to the "file drawer" problem. (2) @Zen also mentioned the case where the author(s) manipulate(s) the data or the models (e.g. d | What causes the discontinuity in the distribution of published p-values at p < .05?
(1) As already mentioned by @PeterFlom, one explanation might be related to the "file drawer" problem. (2) @Zen also mentioned the case where the author(s) manipulate(s) the data or the models (e.g. data dredging). (3) However, we do no... | What causes the discontinuity in the distribution of published p-values at p < .05?
(1) As already mentioned by @PeterFlom, one explanation might be related to the "file drawer" problem. (2) @Zen also mentioned the case where the author(s) manipulate(s) the data or the models (e.g. d |
9,635 | What causes the discontinuity in the distribution of published p-values at p < .05? | One argument that is missing so far is the flexibility of data analysis known as researchers degrees of freedom. In every analysis there are many decisions to be made, where to set the outlier criterion, how to transform the data, and ...
This was recently raised in an influential article by Simmons, Nelson and Simonso... | What causes the discontinuity in the distribution of published p-values at p < .05? | One argument that is missing so far is the flexibility of data analysis known as researchers degrees of freedom. In every analysis there are many decisions to be made, where to set the outlier criteri | What causes the discontinuity in the distribution of published p-values at p < .05?
One argument that is missing so far is the flexibility of data analysis known as researchers degrees of freedom. In every analysis there are many decisions to be made, where to set the outlier criterion, how to transform the data, and .... | What causes the discontinuity in the distribution of published p-values at p < .05?
One argument that is missing so far is the flexibility of data analysis known as researchers degrees of freedom. In every analysis there are many decisions to be made, where to set the outlier criteri |
9,636 | What causes the discontinuity in the distribution of published p-values at p < .05? | I think it is a combination of everything that has already been said. This is very interesting data and I have not thought of looking at p-value distributions like this before. If the null hypothesis is true the p-value would be uniform. But of course with published results we would not see uniformity for many reaso... | What causes the discontinuity in the distribution of published p-values at p < .05? | I think it is a combination of everything that has already been said. This is very interesting data and I have not thought of looking at p-value distributions like this before. If the null hypothesi | What causes the discontinuity in the distribution of published p-values at p < .05?
I think it is a combination of everything that has already been said. This is very interesting data and I have not thought of looking at p-value distributions like this before. If the null hypothesis is true the p-value would be unifo... | What causes the discontinuity in the distribution of published p-values at p < .05?
I think it is a combination of everything that has already been said. This is very interesting data and I have not thought of looking at p-value distributions like this before. If the null hypothesi |
9,637 | Omitted variable bias in logistic regression vs. omitted variable bias in ordinary least squares regression | The case of "attenuation bias" can be more clearly presented if we examine the "probit" model -but the result carry over to the logistic regression also.
Underneath the Conditional Probability Models (Logistic (logit), "probit", and "Linear Probability" models) we can postulate a latent (unobservable) linear regression... | Omitted variable bias in logistic regression vs. omitted variable bias in ordinary least squares reg | The case of "attenuation bias" can be more clearly presented if we examine the "probit" model -but the result carry over to the logistic regression also.
Underneath the Conditional Probability Models | Omitted variable bias in logistic regression vs. omitted variable bias in ordinary least squares regression
The case of "attenuation bias" can be more clearly presented if we examine the "probit" model -but the result carry over to the logistic regression also.
Underneath the Conditional Probability Models (Logistic (l... | Omitted variable bias in logistic regression vs. omitted variable bias in ordinary least squares reg
The case of "attenuation bias" can be more clearly presented if we examine the "probit" model -but the result carry over to the logistic regression also.
Underneath the Conditional Probability Models |
9,638 | What is the non-parametric equivalent of a two-way ANOVA that can include interactions? | When most people think of a non-parametric equivalent of ANOVA, they think of the Kruskal-Wallis test. The Kruskal-Wallis test cannot be applied to a factorial structure, however.
The first workaround to this is to run all of your conditions as a one-way analysis. This does not let you test your factors individuall... | What is the non-parametric equivalent of a two-way ANOVA that can include interactions? | When most people think of a non-parametric equivalent of ANOVA, they think of the Kruskal-Wallis test. The Kruskal-Wallis test cannot be applied to a factorial structure, however.
The first workaro | What is the non-parametric equivalent of a two-way ANOVA that can include interactions?
When most people think of a non-parametric equivalent of ANOVA, they think of the Kruskal-Wallis test. The Kruskal-Wallis test cannot be applied to a factorial structure, however.
The first workaround to this is to run all of you... | What is the non-parametric equivalent of a two-way ANOVA that can include interactions?
When most people think of a non-parametric equivalent of ANOVA, they think of the Kruskal-Wallis test. The Kruskal-Wallis test cannot be applied to a factorial structure, however.
The first workaro |
9,639 | Boosting: why is the learning rate called a regularization parameter? | Suppose you are trying to minimize the objective function via number of iterations. And current value is $100.0$. In given data set, there are no "irreducible errors" and you can minimize the loss to $0.0$ for your training data. Now you have two ways to do it.
The first way is "large learning rate" and few iterations... | Boosting: why is the learning rate called a regularization parameter? | Suppose you are trying to minimize the objective function via number of iterations. And current value is $100.0$. In given data set, there are no "irreducible errors" and you can minimize the loss to | Boosting: why is the learning rate called a regularization parameter?
Suppose you are trying to minimize the objective function via number of iterations. And current value is $100.0$. In given data set, there are no "irreducible errors" and you can minimize the loss to $0.0$ for your training data. Now you have two way... | Boosting: why is the learning rate called a regularization parameter?
Suppose you are trying to minimize the objective function via number of iterations. And current value is $100.0$. In given data set, there are no "irreducible errors" and you can minimize the loss to |
9,640 | Boosting: why is the learning rate called a regularization parameter? | With Newton's method, you update your parameters by subtracting the gradient of the loss divided by the curvature of the loss. In gradient descent optimization, you update your parameters by subtracting the gradient of the loss times the learning rate. In other words, the reciprocal of the learning rate is used in pl... | Boosting: why is the learning rate called a regularization parameter? | With Newton's method, you update your parameters by subtracting the gradient of the loss divided by the curvature of the loss. In gradient descent optimization, you update your parameters by subtract | Boosting: why is the learning rate called a regularization parameter?
With Newton's method, you update your parameters by subtracting the gradient of the loss divided by the curvature of the loss. In gradient descent optimization, you update your parameters by subtracting the gradient of the loss times the learning ra... | Boosting: why is the learning rate called a regularization parameter?
With Newton's method, you update your parameters by subtracting the gradient of the loss divided by the curvature of the loss. In gradient descent optimization, you update your parameters by subtract |
9,641 | Boosting: why is the learning rate called a regularization parameter? | To summarize:
(smaller learning rate, same number of iterations) -> "more regularizations"
(same learning rate, smaller number of iterations) -> "more regularizations"
In simple words, the regularizations effect is coming from the insuffisance of iterations to keep up with the finesse of the search posed by the learn... | Boosting: why is the learning rate called a regularization parameter? | To summarize:
(smaller learning rate, same number of iterations) -> "more regularizations"
(same learning rate, smaller number of iterations) -> "more regularizations"
In simple words, the regulariz | Boosting: why is the learning rate called a regularization parameter?
To summarize:
(smaller learning rate, same number of iterations) -> "more regularizations"
(same learning rate, smaller number of iterations) -> "more regularizations"
In simple words, the regularizations effect is coming from the insuffisance of i... | Boosting: why is the learning rate called a regularization parameter?
To summarize:
(smaller learning rate, same number of iterations) -> "more regularizations"
(same learning rate, smaller number of iterations) -> "more regularizations"
In simple words, the regulariz |
9,642 | Bag-of-Words for Text Classification: Why not just use word frequencies instead of TFIDF? | You're correct that the supervised learner can often be redundant with TF-IDF weighting. Here's the basic outline of why: In one typical form of TF-IDF weighting, the rescaling is logarithmic, so the weighting for a word $w$ in a document $d$ is
$$
\text{TF-IDF}(w,d) = (\text{no. occurrences of $w$ in $d$}) \cdot f(w)... | Bag-of-Words for Text Classification: Why not just use word frequencies instead of TFIDF? | You're correct that the supervised learner can often be redundant with TF-IDF weighting. Here's the basic outline of why: In one typical form of TF-IDF weighting, the rescaling is logarithmic, so the | Bag-of-Words for Text Classification: Why not just use word frequencies instead of TFIDF?
You're correct that the supervised learner can often be redundant with TF-IDF weighting. Here's the basic outline of why: In one typical form of TF-IDF weighting, the rescaling is logarithmic, so the weighting for a word $w$ in a... | Bag-of-Words for Text Classification: Why not just use word frequencies instead of TFIDF?
You're correct that the supervised learner can often be redundant with TF-IDF weighting. Here's the basic outline of why: In one typical form of TF-IDF weighting, the rescaling is logarithmic, so the |
9,643 | Bag-of-Words for Text Classification: Why not just use word frequencies instead of TFIDF? | In the typical case, you could have many more documents in your corpus than labeled documents. That means the IDF can be calculated much more accurately and completely when using the whole corpus.
Next consider the case where the corpus you can get your hands on so far is all labeled or the labeled subset is "big enoug... | Bag-of-Words for Text Classification: Why not just use word frequencies instead of TFIDF? | In the typical case, you could have many more documents in your corpus than labeled documents. That means the IDF can be calculated much more accurately and completely when using the whole corpus.
Nex | Bag-of-Words for Text Classification: Why not just use word frequencies instead of TFIDF?
In the typical case, you could have many more documents in your corpus than labeled documents. That means the IDF can be calculated much more accurately and completely when using the whole corpus.
Next consider the case where the ... | Bag-of-Words for Text Classification: Why not just use word frequencies instead of TFIDF?
In the typical case, you could have many more documents in your corpus than labeled documents. That means the IDF can be calculated much more accurately and completely when using the whole corpus.
Nex |
9,644 | ROC curve for discrete classifiers like SVM: Why do we still call it a "curve"?, Isn't it just a "point"? | Yes, there are situations where the usual receiver operating curve cannot be obtained and only one point exists.
SVMs can be set up so that they output class membership probabilities. These would be the usual value for which a threshold would be varied to produce a receiver operating curve.
Is that what you are looking... | ROC curve for discrete classifiers like SVM: Why do we still call it a "curve"?, Isn't it just a "po | Yes, there are situations where the usual receiver operating curve cannot be obtained and only one point exists.
SVMs can be set up so that they output class membership probabilities. These would be t | ROC curve for discrete classifiers like SVM: Why do we still call it a "curve"?, Isn't it just a "point"?
Yes, there are situations where the usual receiver operating curve cannot be obtained and only one point exists.
SVMs can be set up so that they output class membership probabilities. These would be the usual value... | ROC curve for discrete classifiers like SVM: Why do we still call it a "curve"?, Isn't it just a "po
Yes, there are situations where the usual receiver operating curve cannot be obtained and only one point exists.
SVMs can be set up so that they output class membership probabilities. These would be t |
9,645 | ROC curve for discrete classifiers like SVM: Why do we still call it a "curve"?, Isn't it just a "point"? | Normally, the predicted label $\hat{y}$ from SVM is given by
$\hat{y}=\mbox{sign}({\mathbf w^T x}+b)$, where ${\mathbf w}$ is the SVM-optimized weights of the hyper-plane, and the $b$ is the SVM-optimized intercept. This can also be re-written as follows:
\begin{eqnarray}
\hat{y} & = & \left\{\begin{array}{cc} 0 & \mbo... | ROC curve for discrete classifiers like SVM: Why do we still call it a "curve"?, Isn't it just a "po | Normally, the predicted label $\hat{y}$ from SVM is given by
$\hat{y}=\mbox{sign}({\mathbf w^T x}+b)$, where ${\mathbf w}$ is the SVM-optimized weights of the hyper-plane, and the $b$ is the SVM-optim | ROC curve for discrete classifiers like SVM: Why do we still call it a "curve"?, Isn't it just a "point"?
Normally, the predicted label $\hat{y}$ from SVM is given by
$\hat{y}=\mbox{sign}({\mathbf w^T x}+b)$, where ${\mathbf w}$ is the SVM-optimized weights of the hyper-plane, and the $b$ is the SVM-optimized intercept... | ROC curve for discrete classifiers like SVM: Why do we still call it a "curve"?, Isn't it just a "po
Normally, the predicted label $\hat{y}$ from SVM is given by
$\hat{y}=\mbox{sign}({\mathbf w^T x}+b)$, where ${\mathbf w}$ is the SVM-optimized weights of the hyper-plane, and the $b$ is the SVM-optim |
9,646 | ROC curve for discrete classifiers like SVM: Why do we still call it a "curve"?, Isn't it just a "point"? | The ROC curve plots specificity vs sensitivity which varies with the threshold of a covariate (which may be continuous or discrete). I think you are confusing the covariate with the response and perhaps do not fully understand what an ROC curve is. It is certainly a curve if the covariate is continuous and we look at... | ROC curve for discrete classifiers like SVM: Why do we still call it a "curve"?, Isn't it just a "po | The ROC curve plots specificity vs sensitivity which varies with the threshold of a covariate (which may be continuous or discrete). I think you are confusing the covariate with the response and perh | ROC curve for discrete classifiers like SVM: Why do we still call it a "curve"?, Isn't it just a "point"?
The ROC curve plots specificity vs sensitivity which varies with the threshold of a covariate (which may be continuous or discrete). I think you are confusing the covariate with the response and perhaps do not ful... | ROC curve for discrete classifiers like SVM: Why do we still call it a "curve"?, Isn't it just a "po
The ROC curve plots specificity vs sensitivity which varies with the threshold of a covariate (which may be continuous or discrete). I think you are confusing the covariate with the response and perh |
9,647 | PCA when the dimensionality is greater than the number of samples | I'd look at the problem from a slightly different angle: how complex a model can you afford with only 10 subjects / 100 samples?
And that question I usually answer with: much less than 100 PCs. Note that I work on a different type of data (vibrational spectra), so things may vary a bit. In my field a common set up woul... | PCA when the dimensionality is greater than the number of samples | I'd look at the problem from a slightly different angle: how complex a model can you afford with only 10 subjects / 100 samples?
And that question I usually answer with: much less than 100 PCs. Note t | PCA when the dimensionality is greater than the number of samples
I'd look at the problem from a slightly different angle: how complex a model can you afford with only 10 subjects / 100 samples?
And that question I usually answer with: much less than 100 PCs. Note that I work on a different type of data (vibrational sp... | PCA when the dimensionality is greater than the number of samples
I'd look at the problem from a slightly different angle: how complex a model can you afford with only 10 subjects / 100 samples?
And that question I usually answer with: much less than 100 PCs. Note t |
9,648 | PCA when the dimensionality is greater than the number of samples | If $n$ is the number of points and $p$ is the number of dimensions and $n \leq p$ then the number of principal components with non-zero variance cannot exceed $n$ (when doing PCA on raw data) or $n-1$ (when doing PCA on centered data - as usual). | PCA when the dimensionality is greater than the number of samples | If $n$ is the number of points and $p$ is the number of dimensions and $n \leq p$ then the number of principal components with non-zero variance cannot exceed $n$ (when doing PCA on raw data) or $n-1$ | PCA when the dimensionality is greater than the number of samples
If $n$ is the number of points and $p$ is the number of dimensions and $n \leq p$ then the number of principal components with non-zero variance cannot exceed $n$ (when doing PCA on raw data) or $n-1$ (when doing PCA on centered data - as usual). | PCA when the dimensionality is greater than the number of samples
If $n$ is the number of points and $p$ is the number of dimensions and $n \leq p$ then the number of principal components with non-zero variance cannot exceed $n$ (when doing PCA on raw data) or $n-1$ |
9,649 | PCA when the dimensionality is greater than the number of samples | Coming at this from a different angle:
In PCA, you're approximating the covariance matrix by a $k$-rank approximation (that is, you only keep the top $k$ principal components). If you want to picture this, the covariance vectors are being projected orthogonally down into a lower dimensional linear subspace. Since you'v... | PCA when the dimensionality is greater than the number of samples | Coming at this from a different angle:
In PCA, you're approximating the covariance matrix by a $k$-rank approximation (that is, you only keep the top $k$ principal components). If you want to picture | PCA when the dimensionality is greater than the number of samples
Coming at this from a different angle:
In PCA, you're approximating the covariance matrix by a $k$-rank approximation (that is, you only keep the top $k$ principal components). If you want to picture this, the covariance vectors are being projected ortho... | PCA when the dimensionality is greater than the number of samples
Coming at this from a different angle:
In PCA, you're approximating the covariance matrix by a $k$-rank approximation (that is, you only keep the top $k$ principal components). If you want to picture |
9,650 | What is the difference between episode and epoch in deep Q learning? | one episode = one a sequence of states, actions and rewards, which ends with terminal state. For example, playing an entire game can be considered as one episode, the terminal state being reached when one player loses/wins/draws. Sometime, one may prefer to define one episode as several games (example: "each episode is... | What is the difference between episode and epoch in deep Q learning? | one episode = one a sequence of states, actions and rewards, which ends with terminal state. For example, playing an entire game can be considered as one episode, the terminal state being reached when | What is the difference between episode and epoch in deep Q learning?
one episode = one a sequence of states, actions and rewards, which ends with terminal state. For example, playing an entire game can be considered as one episode, the terminal state being reached when one player loses/wins/draws. Sometime, one may pre... | What is the difference between episode and epoch in deep Q learning?
one episode = one a sequence of states, actions and rewards, which ends with terminal state. For example, playing an entire game can be considered as one episode, the terminal state being reached when |
9,651 | Using correlation as distance metric (for hierarchical clustering) | Requirements for hierarchical clustering
Hierarchical clustering can be used with arbitrary similarity and dissimilarity measures. (Most tools expect a dissimilarity, but will allow negative values - it's up to you to ensure whether small or large valued will be preferred.).
Only methods based on centroids or variance ... | Using correlation as distance metric (for hierarchical clustering) | Requirements for hierarchical clustering
Hierarchical clustering can be used with arbitrary similarity and dissimilarity measures. (Most tools expect a dissimilarity, but will allow negative values - | Using correlation as distance metric (for hierarchical clustering)
Requirements for hierarchical clustering
Hierarchical clustering can be used with arbitrary similarity and dissimilarity measures. (Most tools expect a dissimilarity, but will allow negative values - it's up to you to ensure whether small or large value... | Using correlation as distance metric (for hierarchical clustering)
Requirements for hierarchical clustering
Hierarchical clustering can be used with arbitrary similarity and dissimilarity measures. (Most tools expect a dissimilarity, but will allow negative values - |
9,652 | Using correlation as distance metric (for hierarchical clustering) | I'll expand a bit on the accepted answer to show that in case we've standard-scaled the input data (let's assume it's n-dim), then both euclidean and correlation based distance metrics are just scaled variations of each other.
Euclidean distance for standard-scaled data is as follows:
$$ E_d^2(X, Y) := \lVert X - Y \lV... | Using correlation as distance metric (for hierarchical clustering) | I'll expand a bit on the accepted answer to show that in case we've standard-scaled the input data (let's assume it's n-dim), then both euclidean and correlation based distance metrics are just scaled | Using correlation as distance metric (for hierarchical clustering)
I'll expand a bit on the accepted answer to show that in case we've standard-scaled the input data (let's assume it's n-dim), then both euclidean and correlation based distance metrics are just scaled variations of each other.
Euclidean distance for sta... | Using correlation as distance metric (for hierarchical clustering)
I'll expand a bit on the accepted answer to show that in case we've standard-scaled the input data (let's assume it's n-dim), then both euclidean and correlation based distance metrics are just scaled |
9,653 | How to systematically remove collinear variables (pandas columns) in Python? [closed] | Thanks SpanishBoy - It is a good piece of code.
@ilanman: This checks VIF values and then drops variables whose VIF is more than 5. By "performance", I think he means run time. The above code took me about 3 hours to run on about 300 variables, 5000 rows.
By the way, I have modified it to remove some extra loops. Also,... | How to systematically remove collinear variables (pandas columns) in Python? [closed] | Thanks SpanishBoy - It is a good piece of code.
@ilanman: This checks VIF values and then drops variables whose VIF is more than 5. By "performance", I think he means run time. The above code took me | How to systematically remove collinear variables (pandas columns) in Python? [closed]
Thanks SpanishBoy - It is a good piece of code.
@ilanman: This checks VIF values and then drops variables whose VIF is more than 5. By "performance", I think he means run time. The above code took me about 3 hours to run on about 300 ... | How to systematically remove collinear variables (pandas columns) in Python? [closed]
Thanks SpanishBoy - It is a good piece of code.
@ilanman: This checks VIF values and then drops variables whose VIF is more than 5. By "performance", I think he means run time. The above code took me |
9,654 | How to systematically remove collinear variables (pandas columns) in Python? [closed] | You can try use below code:
from statsmodels.stats.outliers_influence import variance_inflation_factor
def calculate_vif_(X):
'''X - pandas dataframe'''
thresh = 5.0
variables = range(X.shape[1])
for i in np.arange(0, len(variables)):
vif = [variance_inflation_factor(X[variables].values, ix) ... | How to systematically remove collinear variables (pandas columns) in Python? [closed] | You can try use below code:
from statsmodels.stats.outliers_influence import variance_inflation_factor
def calculate_vif_(X):
'''X - pandas dataframe'''
thresh = 5.0
variables = range(X. | How to systematically remove collinear variables (pandas columns) in Python? [closed]
You can try use below code:
from statsmodels.stats.outliers_influence import variance_inflation_factor
def calculate_vif_(X):
'''X - pandas dataframe'''
thresh = 5.0
variables = range(X.shape[1])
for i in np.arange(... | How to systematically remove collinear variables (pandas columns) in Python? [closed]
You can try use below code:
from statsmodels.stats.outliers_influence import variance_inflation_factor
def calculate_vif_(X):
'''X - pandas dataframe'''
thresh = 5.0
variables = range(X. |
9,655 | How to systematically remove collinear variables (pandas columns) in Python? [closed] | I tried SpanishBoy's answer and found serval errors when running it for a data-frame. Here is a debugged solution.
from statsmodels.stats.outliers_influence import variance_inflation_factor
def calculate_vif_(X, thresh=100):
cols = X.columns
variables = np.arange(X.shape[1])
dropped=True
while dropped:
droppe... | How to systematically remove collinear variables (pandas columns) in Python? [closed] | I tried SpanishBoy's answer and found serval errors when running it for a data-frame. Here is a debugged solution.
from statsmodels.stats.outliers_influence import variance_inflation_factor
def | How to systematically remove collinear variables (pandas columns) in Python? [closed]
I tried SpanishBoy's answer and found serval errors when running it for a data-frame. Here is a debugged solution.
from statsmodels.stats.outliers_influence import variance_inflation_factor
def calculate_vif_(X, thresh=100):
col... | How to systematically remove collinear variables (pandas columns) in Python? [closed]
I tried SpanishBoy's answer and found serval errors when running it for a data-frame. Here is a debugged solution.
from statsmodels.stats.outliers_influence import variance_inflation_factor
def |
9,656 | Using bootstrap under H0 to perform a test for the difference of two means: replacement within the groups or within the pooled sample | Here is my take on it, based on chapter 16 of Efron's and Tibshirani's An Introduction to the bootstrap (page 220-224). The short of it is that your second bootstrap algorithm was implemented wrongly, but the general idea is correct.
When conducting bootstrap tests, one has to make sure that the re-sampling method gene... | Using bootstrap under H0 to perform a test for the difference of two means: replacement within the g | Here is my take on it, based on chapter 16 of Efron's and Tibshirani's An Introduction to the bootstrap (page 220-224). The short of it is that your second bootstrap algorithm was implemented wrongly, | Using bootstrap under H0 to perform a test for the difference of two means: replacement within the groups or within the pooled sample
Here is my take on it, based on chapter 16 of Efron's and Tibshirani's An Introduction to the bootstrap (page 220-224). The short of it is that your second bootstrap algorithm was implem... | Using bootstrap under H0 to perform a test for the difference of two means: replacement within the g
Here is my take on it, based on chapter 16 of Efron's and Tibshirani's An Introduction to the bootstrap (page 220-224). The short of it is that your second bootstrap algorithm was implemented wrongly, |
9,657 | Differences between MANOVA and Repeated Measures ANOVA? | Having several repeated-measures DVs one can apply a univariate approach (also called Repeated Measures sensu stricto or split-plot approach) or multivariate approach (or MANOVA). In univariate approach, RM levels are treated as deviations from one variable, their average level. In multivariate approach, RM levels are ... | Differences between MANOVA and Repeated Measures ANOVA? | Having several repeated-measures DVs one can apply a univariate approach (also called Repeated Measures sensu stricto or split-plot approach) or multivariate approach (or MANOVA). In univariate approa | Differences between MANOVA and Repeated Measures ANOVA?
Having several repeated-measures DVs one can apply a univariate approach (also called Repeated Measures sensu stricto or split-plot approach) or multivariate approach (or MANOVA). In univariate approach, RM levels are treated as deviations from one variable, their... | Differences between MANOVA and Repeated Measures ANOVA?
Having several repeated-measures DVs one can apply a univariate approach (also called Repeated Measures sensu stricto or split-plot approach) or multivariate approach (or MANOVA). In univariate approa |
9,658 | Differences between MANOVA and Repeated Measures ANOVA? | Geometrically, MANOVA rejects iff the mean (difference) vector lays outside of an ellipsoid. Repeated measures ANOVA, say, with $d$ repeated measures per subject, rejects iff the $d$-dimensional mean (difference) vector lays outside of a sphere. The shape of the ellipsoid is determined by the covariance matrix. It can ... | Differences between MANOVA and Repeated Measures ANOVA? | Geometrically, MANOVA rejects iff the mean (difference) vector lays outside of an ellipsoid. Repeated measures ANOVA, say, with $d$ repeated measures per subject, rejects iff the $d$-dimensional mean | Differences between MANOVA and Repeated Measures ANOVA?
Geometrically, MANOVA rejects iff the mean (difference) vector lays outside of an ellipsoid. Repeated measures ANOVA, say, with $d$ repeated measures per subject, rejects iff the $d$-dimensional mean (difference) vector lays outside of a sphere. The shape of the e... | Differences between MANOVA and Repeated Measures ANOVA?
Geometrically, MANOVA rejects iff the mean (difference) vector lays outside of an ellipsoid. Repeated measures ANOVA, say, with $d$ repeated measures per subject, rejects iff the $d$-dimensional mean |
9,659 | Differences between MANOVA and Repeated Measures ANOVA? | I prefer a repeated measures model. Not only is it easier to interpret the results, it is more flexible in that you can specify a covariance structure.
This reference may be of use as it works through an example:
Mixed or MANOVA | Differences between MANOVA and Repeated Measures ANOVA? | I prefer a repeated measures model. Not only is it easier to interpret the results, it is more flexible in that you can specify a covariance structure.
This reference may be of use as it works throug | Differences between MANOVA and Repeated Measures ANOVA?
I prefer a repeated measures model. Not only is it easier to interpret the results, it is more flexible in that you can specify a covariance structure.
This reference may be of use as it works through an example:
Mixed or MANOVA | Differences between MANOVA and Repeated Measures ANOVA?
I prefer a repeated measures model. Not only is it easier to interpret the results, it is more flexible in that you can specify a covariance structure.
This reference may be of use as it works throug |
9,660 | How does leave-one-out cross-validation work? How to select the final model out of $n$ different models? | It is best to think of cross-validation as a way of estimating the generalisation performance of models generated by a particular procedure, rather than of the model itself. Leave-one-out cross-validation is essentially an estimate of the generalisation performance of a model trained on $n-1$ samples of data, which is... | How does leave-one-out cross-validation work? How to select the final model out of $n$ different mod | It is best to think of cross-validation as a way of estimating the generalisation performance of models generated by a particular procedure, rather than of the model itself. Leave-one-out cross-valid | How does leave-one-out cross-validation work? How to select the final model out of $n$ different models?
It is best to think of cross-validation as a way of estimating the generalisation performance of models generated by a particular procedure, rather than of the model itself. Leave-one-out cross-validation is essent... | How does leave-one-out cross-validation work? How to select the final model out of $n$ different mod
It is best to think of cross-validation as a way of estimating the generalisation performance of models generated by a particular procedure, rather than of the model itself. Leave-one-out cross-valid |
9,661 | How to cope with exploratory data analysis and data dredging in small-sample studies? | I think the main thing is to be honest when reporting such results that they were unexpected findings from EDA and not part of the initial analysis plan based on an a priori hypothesis. Some people like to label such results 'hypothesis generating': e.g. the first hit from a search for this phrase on Google Scholar inc... | How to cope with exploratory data analysis and data dredging in small-sample studies? | I think the main thing is to be honest when reporting such results that they were unexpected findings from EDA and not part of the initial analysis plan based on an a priori hypothesis. Some people li | How to cope with exploratory data analysis and data dredging in small-sample studies?
I think the main thing is to be honest when reporting such results that they were unexpected findings from EDA and not part of the initial analysis plan based on an a priori hypothesis. Some people like to label such results 'hypothes... | How to cope with exploratory data analysis and data dredging in small-sample studies?
I think the main thing is to be honest when reporting such results that they were unexpected findings from EDA and not part of the initial analysis plan based on an a priori hypothesis. Some people li |
9,662 | How to cope with exploratory data analysis and data dredging in small-sample studies? | I just drop some references about data dredging and clinical studies for the interested reader. This is intended to extend @onestop's fine answer. I tried to avoid articles focusing only on multiple comparisons or design issues, although studies with multiple endpoints continue to present challenging and controversial ... | How to cope with exploratory data analysis and data dredging in small-sample studies? | I just drop some references about data dredging and clinical studies for the interested reader. This is intended to extend @onestop's fine answer. I tried to avoid articles focusing only on multiple c | How to cope with exploratory data analysis and data dredging in small-sample studies?
I just drop some references about data dredging and clinical studies for the interested reader. This is intended to extend @onestop's fine answer. I tried to avoid articles focusing only on multiple comparisons or design issues, altho... | How to cope with exploratory data analysis and data dredging in small-sample studies?
I just drop some references about data dredging and clinical studies for the interested reader. This is intended to extend @onestop's fine answer. I tried to avoid articles focusing only on multiple c |
9,663 | How to handle ordinal categorical variable as independent variable | The problem with ordinal independent variable is that since, by definition, the true metric intervals between its levels are not known, no appropriate type relationship - apart from umbrella "monotonic" - can be assumed apriori. We have to do something about it, for example - to "screen or to combine variants" or to "p... | How to handle ordinal categorical variable as independent variable | The problem with ordinal independent variable is that since, by definition, the true metric intervals between its levels are not known, no appropriate type relationship - apart from umbrella "monotoni | How to handle ordinal categorical variable as independent variable
The problem with ordinal independent variable is that since, by definition, the true metric intervals between its levels are not known, no appropriate type relationship - apart from umbrella "monotonic" - can be assumed apriori. We have to do something ... | How to handle ordinal categorical variable as independent variable
The problem with ordinal independent variable is that since, by definition, the true metric intervals between its levels are not known, no appropriate type relationship - apart from umbrella "monotoni |
9,664 | How to handle ordinal categorical variable as independent variable | Just to add to the other excellent answers: A modern way of handling it could be via an additive model, representing the ordinal independent variable via a spline. If you are quite sure the effect of the variable is monotone, you could restrict to a monotone spline. (For an example of monotone splines in use, see Loo... | How to handle ordinal categorical variable as independent variable | Just to add to the other excellent answers: A modern way of handling it could be via an additive model, representing the ordinal independent variable via a spline. If you are quite sure the effect of | How to handle ordinal categorical variable as independent variable
Just to add to the other excellent answers: A modern way of handling it could be via an additive model, representing the ordinal independent variable via a spline. If you are quite sure the effect of the variable is monotone, you could restrict to a mo... | How to handle ordinal categorical variable as independent variable
Just to add to the other excellent answers: A modern way of handling it could be via an additive model, representing the ordinal independent variable via a spline. If you are quite sure the effect of |
9,665 | How to handle ordinal categorical variable as independent variable | There have been a lot of compromises that handwave the relationship. The default polynomial contrasts are far from ideal, although I'm sure some would vehemently argue this. I have found that stairstep contrasts manage to capture a monotonic, ordered variable's effects quite well.
ordered_factor <- function(fact_var) {... | How to handle ordinal categorical variable as independent variable | There have been a lot of compromises that handwave the relationship. The default polynomial contrasts are far from ideal, although I'm sure some would vehemently argue this. I have found that stairste | How to handle ordinal categorical variable as independent variable
There have been a lot of compromises that handwave the relationship. The default polynomial contrasts are far from ideal, although I'm sure some would vehemently argue this. I have found that stairstep contrasts manage to capture a monotonic, ordered va... | How to handle ordinal categorical variable as independent variable
There have been a lot of compromises that handwave the relationship. The default polynomial contrasts are far from ideal, although I'm sure some would vehemently argue this. I have found that stairste |
9,666 | How to handle ordinal categorical variable as independent variable | You need dummy variables but you need $k-1$ dummy variables, where $k$ is the number of potential responses. In your case with 5 response values (1-5) you would create 4 dummy variables. When a response is "5" your four dummy variables would be all 0s. Make sense? | How to handle ordinal categorical variable as independent variable | You need dummy variables but you need $k-1$ dummy variables, where $k$ is the number of potential responses. In your case with 5 response values (1-5) you would create 4 dummy variables. When a respon | How to handle ordinal categorical variable as independent variable
You need dummy variables but you need $k-1$ dummy variables, where $k$ is the number of potential responses. In your case with 5 response values (1-5) you would create 4 dummy variables. When a response is "5" your four dummy variables would be all 0s. ... | How to handle ordinal categorical variable as independent variable
You need dummy variables but you need $k-1$ dummy variables, where $k$ is the number of potential responses. In your case with 5 response values (1-5) you would create 4 dummy variables. When a respon |
9,667 | Deep belief networks or Deep Boltzmann Machines? | Although Deep Belief Networks (DBNs) and Deep Boltzmann Machines (DBMs) diagrammatically look very similar, they are actually qualitatively very different. This is because DBNs are directed and DBMs are undirected. If we wanted to fit them into the broader ML picture we could say DBNs are sigmoid belief networks with m... | Deep belief networks or Deep Boltzmann Machines? | Although Deep Belief Networks (DBNs) and Deep Boltzmann Machines (DBMs) diagrammatically look very similar, they are actually qualitatively very different. This is because DBNs are directed and DBMs a | Deep belief networks or Deep Boltzmann Machines?
Although Deep Belief Networks (DBNs) and Deep Boltzmann Machines (DBMs) diagrammatically look very similar, they are actually qualitatively very different. This is because DBNs are directed and DBMs are undirected. If we wanted to fit them into the broader ML picture we ... | Deep belief networks or Deep Boltzmann Machines?
Although Deep Belief Networks (DBNs) and Deep Boltzmann Machines (DBMs) diagrammatically look very similar, they are actually qualitatively very different. This is because DBNs are directed and DBMs a |
9,668 | Deep belief networks or Deep Boltzmann Machines? | Both are probabilistic graphical models consisting of stacked layers of RBMs. The difference is in how these layers are connected.
This link makes it fairly clear: http://jmlr.org/proceedings/papers/v5/salakhutdinov09a/salakhutdinov09a.pdf. Figure 2 and Section 3.1 are particularly relevant.
To summarise:
In a DBN the... | Deep belief networks or Deep Boltzmann Machines? | Both are probabilistic graphical models consisting of stacked layers of RBMs. The difference is in how these layers are connected.
This link makes it fairly clear: http://jmlr.org/proceedings/papers/ | Deep belief networks or Deep Boltzmann Machines?
Both are probabilistic graphical models consisting of stacked layers of RBMs. The difference is in how these layers are connected.
This link makes it fairly clear: http://jmlr.org/proceedings/papers/v5/salakhutdinov09a/salakhutdinov09a.pdf. Figure 2 and Section 3.1 are ... | Deep belief networks or Deep Boltzmann Machines?
Both are probabilistic graphical models consisting of stacked layers of RBMs. The difference is in how these layers are connected.
This link makes it fairly clear: http://jmlr.org/proceedings/papers/ |
9,669 | Examples of errors in MCMC algorithms | 1. Marginal Likelihood and Harmonic mean estimator
The marginal likelihood is defined as the normalising constant of the posterior distribution
$$p({\bf x})=\int_{\Theta}p({\bf x}\vert\theta)p(\theta)d\theta.$$
The importance of this quantity comes from the role it plays in model comparison via Bayes factors.
Several m... | Examples of errors in MCMC algorithms | 1. Marginal Likelihood and Harmonic mean estimator
The marginal likelihood is defined as the normalising constant of the posterior distribution
$$p({\bf x})=\int_{\Theta}p({\bf x}\vert\theta)p(\theta) | Examples of errors in MCMC algorithms
1. Marginal Likelihood and Harmonic mean estimator
The marginal likelihood is defined as the normalising constant of the posterior distribution
$$p({\bf x})=\int_{\Theta}p({\bf x}\vert\theta)p(\theta)d\theta.$$
The importance of this quantity comes from the role it plays in model c... | Examples of errors in MCMC algorithms
1. Marginal Likelihood and Harmonic mean estimator
The marginal likelihood is defined as the normalising constant of the posterior distribution
$$p({\bf x})=\int_{\Theta}p({\bf x}\vert\theta)p(\theta) |
9,670 | Examples of errors in MCMC algorithms | Darren Wilkinson on his blog gives a detailed example of a common mistake in random walk Metropolis-Hastings. I recommend reading it in full, but here is the tl;dr version.
If the target distribution is positive (like Gamma distributions etc) in one dimension, it is tempting to reject proposals that have a negative val... | Examples of errors in MCMC algorithms | Darren Wilkinson on his blog gives a detailed example of a common mistake in random walk Metropolis-Hastings. I recommend reading it in full, but here is the tl;dr version.
If the target distribution | Examples of errors in MCMC algorithms
Darren Wilkinson on his blog gives a detailed example of a common mistake in random walk Metropolis-Hastings. I recommend reading it in full, but here is the tl;dr version.
If the target distribution is positive (like Gamma distributions etc) in one dimension, it is tempting to rej... | Examples of errors in MCMC algorithms
Darren Wilkinson on his blog gives a detailed example of a common mistake in random walk Metropolis-Hastings. I recommend reading it in full, but here is the tl;dr version.
If the target distribution |
9,671 | Examples of errors in MCMC algorithms | A very clear case (connected with the marginal likelihood approximation mentioned in the first answer) where true convergence is the example of the problem of label switching in mixture models coupled with the use of Chib's (1995) estimator. As pointed out by Radford Neal (1999), if the MCMC chain does not converge cor... | Examples of errors in MCMC algorithms | A very clear case (connected with the marginal likelihood approximation mentioned in the first answer) where true convergence is the example of the problem of label switching in mixture models coupled | Examples of errors in MCMC algorithms
A very clear case (connected with the marginal likelihood approximation mentioned in the first answer) where true convergence is the example of the problem of label switching in mixture models coupled with the use of Chib's (1995) estimator. As pointed out by Radford Neal (1999), i... | Examples of errors in MCMC algorithms
A very clear case (connected with the marginal likelihood approximation mentioned in the first answer) where true convergence is the example of the problem of label switching in mixture models coupled |
9,672 | How is empirical Bayes valid? | I think it's important to remember that different methods are good for different things, and significance testing isn't all there is in the world of statistics.
1 and 3) EB is probably not a valid hypothesis testing procedure, but it's also not meant to be.
Validity could be many things, but you're talking about Rigoro... | How is empirical Bayes valid? | I think it's important to remember that different methods are good for different things, and significance testing isn't all there is in the world of statistics.
1 and 3) EB is probably not a valid hyp | How is empirical Bayes valid?
I think it's important to remember that different methods are good for different things, and significance testing isn't all there is in the world of statistics.
1 and 3) EB is probably not a valid hypothesis testing procedure, but it's also not meant to be.
Validity could be many things, b... | How is empirical Bayes valid?
I think it's important to remember that different methods are good for different things, and significance testing isn't all there is in the world of statistics.
1 and 3) EB is probably not a valid hyp |
9,673 | How to fix non-convergence in LogisticRegressionCV | You can start by applying program's suggestion to increase max_iter parameter; but have in mind that it is also possible that your data simply can't be fit by a logistic model. | How to fix non-convergence in LogisticRegressionCV | You can start by applying program's suggestion to increase max_iter parameter; but have in mind that it is also possible that your data simply can't be fit by a logistic model. | How to fix non-convergence in LogisticRegressionCV
You can start by applying program's suggestion to increase max_iter parameter; but have in mind that it is also possible that your data simply can't be fit by a logistic model. | How to fix non-convergence in LogisticRegressionCV
You can start by applying program's suggestion to increase max_iter parameter; but have in mind that it is also possible that your data simply can't be fit by a logistic model. |
9,674 | How to fix non-convergence in LogisticRegressionCV | Change 'solver' to 'sag' or 'saga'. This will increase to accommodate the larger numbers and remove the warning. | How to fix non-convergence in LogisticRegressionCV | Change 'solver' to 'sag' or 'saga'. This will increase to accommodate the larger numbers and remove the warning. | How to fix non-convergence in LogisticRegressionCV
Change 'solver' to 'sag' or 'saga'. This will increase to accommodate the larger numbers and remove the warning. | How to fix non-convergence in LogisticRegressionCV
Change 'solver' to 'sag' or 'saga'. This will increase to accommodate the larger numbers and remove the warning. |
9,675 | Under what circumstances is an MA process or AR process appropriate? | One important and useful result is the Wold representation theorem (sometimes called the Wold decomposition), which says that every covariance-stationary time series $Y_{t}$ can be written as the sum of two time series, one deterministic and one stochastic.
$Y_t=\mu_t+\sum_{j=0}^\infty b_j \varepsilon_{t-j}\,$, where $... | Under what circumstances is an MA process or AR process appropriate? | One important and useful result is the Wold representation theorem (sometimes called the Wold decomposition), which says that every covariance-stationary time series $Y_{t}$ can be written as the sum | Under what circumstances is an MA process or AR process appropriate?
One important and useful result is the Wold representation theorem (sometimes called the Wold decomposition), which says that every covariance-stationary time series $Y_{t}$ can be written as the sum of two time series, one deterministic and one stoch... | Under what circumstances is an MA process or AR process appropriate?
One important and useful result is the Wold representation theorem (sometimes called the Wold decomposition), which says that every covariance-stationary time series $Y_{t}$ can be written as the sum |
9,676 | Under what circumstances is an MA process or AR process appropriate? | I can provide what I think is a compelling answer to the first part of the question ("whence MA?") but am presently pondering an equally compelling answer to the second part of the question ("whence AR?").
Consider a series consisting of the closing price (adjusted for splits and dividends) of a stock on consecutive ... | Under what circumstances is an MA process or AR process appropriate? | I can provide what I think is a compelling answer to the first part of the question ("whence MA?") but am presently pondering an equally compelling answer to the second part of the question ("whence A | Under what circumstances is an MA process or AR process appropriate?
I can provide what I think is a compelling answer to the first part of the question ("whence MA?") but am presently pondering an equally compelling answer to the second part of the question ("whence AR?").
Consider a series consisting of the closing... | Under what circumstances is an MA process or AR process appropriate?
I can provide what I think is a compelling answer to the first part of the question ("whence MA?") but am presently pondering an equally compelling answer to the second part of the question ("whence A |
9,677 | Under what circumstances is an MA process or AR process appropriate? | So you have a univariate time series and you want model it/forecast it, right? You have chosen to use an ARIMA type model.
The parameters of the depend on what's best for your dataset. But how do you find out?
A recent approach is "Automatic time series forecasting" by Hyndman & Khandakar (2008) (pdf).
The algorithm tr... | Under what circumstances is an MA process or AR process appropriate? | So you have a univariate time series and you want model it/forecast it, right? You have chosen to use an ARIMA type model.
The parameters of the depend on what's best for your dataset. But how do you | Under what circumstances is an MA process or AR process appropriate?
So you have a univariate time series and you want model it/forecast it, right? You have chosen to use an ARIMA type model.
The parameters of the depend on what's best for your dataset. But how do you find out?
A recent approach is "Automatic time seri... | Under what circumstances is an MA process or AR process appropriate?
So you have a univariate time series and you want model it/forecast it, right? You have chosen to use an ARIMA type model.
The parameters of the depend on what's best for your dataset. But how do you |
9,678 | Comparing levels of factors after a GLM in R | Anne, I will shorty explain how to do such multiple comparisons in general. Why this doesn't work in your specific case, I don't know; I'm sorry.
Edit: Nowadays, I'd recommend using the emmeans package to do pairwise comparisons of the marginal means. Another possibility is the multcomp package and the function glht, w... | Comparing levels of factors after a GLM in R | Anne, I will shorty explain how to do such multiple comparisons in general. Why this doesn't work in your specific case, I don't know; I'm sorry.
Edit: Nowadays, I'd recommend using the emmeans packag | Comparing levels of factors after a GLM in R
Anne, I will shorty explain how to do such multiple comparisons in general. Why this doesn't work in your specific case, I don't know; I'm sorry.
Edit: Nowadays, I'd recommend using the emmeans package to do pairwise comparisons of the marginal means. Another possibility is ... | Comparing levels of factors after a GLM in R
Anne, I will shorty explain how to do such multiple comparisons in general. Why this doesn't work in your specific case, I don't know; I'm sorry.
Edit: Nowadays, I'd recommend using the emmeans packag |
9,679 | In a multi-level model, what are the practical implications of estimating versus not-estimating random effect correlation parameters? | Consider the sleepstudy data, included in lme4. Bates discusses this in his online book about lme4. In chapter 3, he considers two models for the data.
$$M0: \textrm{Reaction} \sim 1 + \textrm{Days} + (1|\textrm{Subject}) +(0+\textrm{Days}|\textrm{Subject}) $$
and
$$MA: \textrm{Reaction} \sim 1 + \textrm{Days} + (\tex... | In a multi-level model, what are the practical implications of estimating versus not-estimating rand | Consider the sleepstudy data, included in lme4. Bates discusses this in his online book about lme4. In chapter 3, he considers two models for the data.
$$M0: \textrm{Reaction} \sim 1 + \textrm{Days} + | In a multi-level model, what are the practical implications of estimating versus not-estimating random effect correlation parameters?
Consider the sleepstudy data, included in lme4. Bates discusses this in his online book about lme4. In chapter 3, he considers two models for the data.
$$M0: \textrm{Reaction} \sim 1 + \... | In a multi-level model, what are the practical implications of estimating versus not-estimating rand
Consider the sleepstudy data, included in lme4. Bates discusses this in his online book about lme4. In chapter 3, he considers two models for the data.
$$M0: \textrm{Reaction} \sim 1 + \textrm{Days} + |
9,680 | In a multi-level model, what are the practical implications of estimating versus not-estimating random effect correlation parameters? | Placidia has already provided a thorough answer using simulated data based on the sleepstudy dataset. Here is another (less rigorous) answer that also uses the sleepstudy data.
We see that one can affect the estimated correlation between the random intercept and random slope by "shifting" the random predictor variable.... | In a multi-level model, what are the practical implications of estimating versus not-estimating rand | Placidia has already provided a thorough answer using simulated data based on the sleepstudy dataset. Here is another (less rigorous) answer that also uses the sleepstudy data.
We see that one can aff | In a multi-level model, what are the practical implications of estimating versus not-estimating random effect correlation parameters?
Placidia has already provided a thorough answer using simulated data based on the sleepstudy dataset. Here is another (less rigorous) answer that also uses the sleepstudy data.
We see th... | In a multi-level model, what are the practical implications of estimating versus not-estimating rand
Placidia has already provided a thorough answer using simulated data based on the sleepstudy dataset. Here is another (less rigorous) answer that also uses the sleepstudy data.
We see that one can aff |
9,681 | Kolmogorov-Smirnov with discrete data: What is proper use of dgof::ks.test in R? | This is an answer to @jbrucks extension (but answers the original as well).
One general test of whether 2 samples come from the same population/distribution or if there is a difference is the permutation test. Choose a statistic of interest, this could be the KS test statistic or the difference of means or the differe... | Kolmogorov-Smirnov with discrete data: What is proper use of dgof::ks.test in R? | This is an answer to @jbrucks extension (but answers the original as well).
One general test of whether 2 samples come from the same population/distribution or if there is a difference is the permutat | Kolmogorov-Smirnov with discrete data: What is proper use of dgof::ks.test in R?
This is an answer to @jbrucks extension (but answers the original as well).
One general test of whether 2 samples come from the same population/distribution or if there is a difference is the permutation test. Choose a statistic of intere... | Kolmogorov-Smirnov with discrete data: What is proper use of dgof::ks.test in R?
This is an answer to @jbrucks extension (but answers the original as well).
One general test of whether 2 samples come from the same population/distribution or if there is a difference is the permutat |
9,682 | How does support vector regression work intuitively? | In short: Maximising the margin can more generally be seen as regularising the solution by minimising $w$ (which is essentially minimising model complexity) this is done both in the classification and regression. But in the case of classification this minimisation is done under the condition that all examples are class... | How does support vector regression work intuitively? | In short: Maximising the margin can more generally be seen as regularising the solution by minimising $w$ (which is essentially minimising model complexity) this is done both in the classification and | How does support vector regression work intuitively?
In short: Maximising the margin can more generally be seen as regularising the solution by minimising $w$ (which is essentially minimising model complexity) this is done both in the classification and regression. But in the case of classification this minimisation is... | How does support vector regression work intuitively?
In short: Maximising the margin can more generally be seen as regularising the solution by minimising $w$ (which is essentially minimising model complexity) this is done both in the classification and |
9,683 | How does support vector regression work intuitively? | In SVM for classification problem we actually try to separate the class as far as possible from the separating line (Hyperplane) and unlike logistic regression, we create a safety boundary from both sides of the hyperplane (different between logistic regression and SVM classification is in their loss function).
Eventu... | How does support vector regression work intuitively? | In SVM for classification problem we actually try to separate the class as far as possible from the separating line (Hyperplane) and unlike logistic regression, we create a safety boundary from both s | How does support vector regression work intuitively?
In SVM for classification problem we actually try to separate the class as far as possible from the separating line (Hyperplane) and unlike logistic regression, we create a safety boundary from both sides of the hyperplane (different between logistic regression and S... | How does support vector regression work intuitively?
In SVM for classification problem we actually try to separate the class as far as possible from the separating line (Hyperplane) and unlike logistic regression, we create a safety boundary from both s |
9,684 | LSA vs. PCA (document clustering) | PCA and LSA are both analyses which use SVD. PCA is a general class of analysis and could in principle be applied to enumerated text corpora in a variety of ways. In contrast LSA is a very clearly specified means of analyzing and reducing text. Both are leveraging the idea that meaning can be extracted from context. ... | LSA vs. PCA (document clustering) | PCA and LSA are both analyses which use SVD. PCA is a general class of analysis and could in principle be applied to enumerated text corpora in a variety of ways. In contrast LSA is a very clearly sp | LSA vs. PCA (document clustering)
PCA and LSA are both analyses which use SVD. PCA is a general class of analysis and could in principle be applied to enumerated text corpora in a variety of ways. In contrast LSA is a very clearly specified means of analyzing and reducing text. Both are leveraging the idea that meani... | LSA vs. PCA (document clustering)
PCA and LSA are both analyses which use SVD. PCA is a general class of analysis and could in principle be applied to enumerated text corpora in a variety of ways. In contrast LSA is a very clearly sp |
9,685 | LSA vs. PCA (document clustering) | LSI is computed on the term-document matrix, while PCA is calculated on the covariance matrix, which means LSI tries to find best linear subspace to describe the data set, while PCA tries to find the best parallel linear subspace. | LSA vs. PCA (document clustering) | LSI is computed on the term-document matrix, while PCA is calculated on the covariance matrix, which means LSI tries to find best linear subspace to describe the data set, while PCA tries to find the | LSA vs. PCA (document clustering)
LSI is computed on the term-document matrix, while PCA is calculated on the covariance matrix, which means LSI tries to find best linear subspace to describe the data set, while PCA tries to find the best parallel linear subspace. | LSA vs. PCA (document clustering)
LSI is computed on the term-document matrix, while PCA is calculated on the covariance matrix, which means LSI tries to find best linear subspace to describe the data set, while PCA tries to find the |
9,686 | LSA vs. PCA (document clustering) | Just some extension to russellpierce's answer.
1) Essentially LSA is PCA applied to text data. When using SVD for PCA, it's not applied to the covariance matrix but the feature-sample matrix directly, which is just the term-document matrix in LSA. The difference is PCA often requires feature-wise normalization for th... | LSA vs. PCA (document clustering) | Just some extension to russellpierce's answer.
1) Essentially LSA is PCA applied to text data. When using SVD for PCA, it's not applied to the covariance matrix but the feature-sample matrix directl | LSA vs. PCA (document clustering)
Just some extension to russellpierce's answer.
1) Essentially LSA is PCA applied to text data. When using SVD for PCA, it's not applied to the covariance matrix but the feature-sample matrix directly, which is just the term-document matrix in LSA. The difference is PCA often requires... | LSA vs. PCA (document clustering)
Just some extension to russellpierce's answer.
1) Essentially LSA is PCA applied to text data. When using SVD for PCA, it's not applied to the covariance matrix but the feature-sample matrix directl |
9,687 | How to do dimensionality reduction in R | Lets assume mat_pages[] contains pages in the columns (which you want to cluster) and individuals in the rows. You can cluster pages based on individual data in Rby using the following command:
pc <- prcomp(x=mat_pages,center=TRUE,scale=TRUE)
The loadings matrix is the matrix of eigenvectors of the SVD decompositio... | How to do dimensionality reduction in R | Lets assume mat_pages[] contains pages in the columns (which you want to cluster) and individuals in the rows. You can cluster pages based on individual data in Rby using the following command:
pc | How to do dimensionality reduction in R
Lets assume mat_pages[] contains pages in the columns (which you want to cluster) and individuals in the rows. You can cluster pages based on individual data in Rby using the following command:
pc <- prcomp(x=mat_pages,center=TRUE,scale=TRUE)
The loadings matrix is the matrix... | How to do dimensionality reduction in R
Lets assume mat_pages[] contains pages in the columns (which you want to cluster) and individuals in the rows. You can cluster pages based on individual data in Rby using the following command:
pc |
9,688 | How to do dimensionality reduction in R | It is certainly a clustering problem. Check out Rs cluster package to get an overview of algorithm options (pam and agnes are the best options to start; they represent two main streams in clustering -- centroids and hierarchical).
The main problem to use clustering on your data is to define a good similarity measure be... | How to do dimensionality reduction in R | It is certainly a clustering problem. Check out Rs cluster package to get an overview of algorithm options (pam and agnes are the best options to start; they represent two main streams in clustering - | How to do dimensionality reduction in R
It is certainly a clustering problem. Check out Rs cluster package to get an overview of algorithm options (pam and agnes are the best options to start; they represent two main streams in clustering -- centroids and hierarchical).
The main problem to use clustering on your data i... | How to do dimensionality reduction in R
It is certainly a clustering problem. Check out Rs cluster package to get an overview of algorithm options (pam and agnes are the best options to start; they represent two main streams in clustering - |
9,689 | How to do dimensionality reduction in R | Dimensionality reduction is basically applying clustering algorithm to the attributes (columns). Because of the fairly large dimensionality of your dataset, you might try to use SOM (self-organizing map/Kohonen net) to create a map for individuals or pages. You can then seen whether the are meaningful (interpretable) p... | How to do dimensionality reduction in R | Dimensionality reduction is basically applying clustering algorithm to the attributes (columns). Because of the fairly large dimensionality of your dataset, you might try to use SOM (self-organizing m | How to do dimensionality reduction in R
Dimensionality reduction is basically applying clustering algorithm to the attributes (columns). Because of the fairly large dimensionality of your dataset, you might try to use SOM (self-organizing map/Kohonen net) to create a map for individuals or pages. You can then seen whet... | How to do dimensionality reduction in R
Dimensionality reduction is basically applying clustering algorithm to the attributes (columns). Because of the fairly large dimensionality of your dataset, you might try to use SOM (self-organizing m |
9,690 | How to do dimensionality reduction in R | If you do PCA, my advice would be to briefly screen other components than only the first two. Once you have the prcomp object pc (see suncoolsu's post), you can plot(pc) to see the amount of variance attributed to the different principal components. You can also easily visualise several (usually three or four) of them ... | How to do dimensionality reduction in R | If you do PCA, my advice would be to briefly screen other components than only the first two. Once you have the prcomp object pc (see suncoolsu's post), you can plot(pc) to see the amount of variance | How to do dimensionality reduction in R
If you do PCA, my advice would be to briefly screen other components than only the first two. Once you have the prcomp object pc (see suncoolsu's post), you can plot(pc) to see the amount of variance attributed to the different principal components. You can also easily visualise ... | How to do dimensionality reduction in R
If you do PCA, my advice would be to briefly screen other components than only the first two. Once you have the prcomp object pc (see suncoolsu's post), you can plot(pc) to see the amount of variance |
9,691 | How to construct a 95% confidence interval of the difference between medians? | The bootstrap procedure that you describe should be valid. However, it's important to keep in mind that, like normal-based 95% CI, a bootstrap confidence interval is only guaranteed to have correct coverage asymptotically. One nice thing about working with the median or other quantiles is that you can construct exact f... | How to construct a 95% confidence interval of the difference between medians? | The bootstrap procedure that you describe should be valid. However, it's important to keep in mind that, like normal-based 95% CI, a bootstrap confidence interval is only guaranteed to have correct co | How to construct a 95% confidence interval of the difference between medians?
The bootstrap procedure that you describe should be valid. However, it's important to keep in mind that, like normal-based 95% CI, a bootstrap confidence interval is only guaranteed to have correct coverage asymptotically. One nice thing abou... | How to construct a 95% confidence interval of the difference between medians?
The bootstrap procedure that you describe should be valid. However, it's important to keep in mind that, like normal-based 95% CI, a bootstrap confidence interval is only guaranteed to have correct co |
9,692 | How to construct a 95% confidence interval of the difference between medians? | You can also try the method suggested in http://www.ncbi.nlm.nih.gov/pubmed/12243307 (Bonett, Price; 2002) as a simpler (at least computationally, I think) alternative. Good question, by the way. | How to construct a 95% confidence interval of the difference between medians? | You can also try the method suggested in http://www.ncbi.nlm.nih.gov/pubmed/12243307 (Bonett, Price; 2002) as a simpler (at least computationally, I think) alternative. Good question, by the way. | How to construct a 95% confidence interval of the difference between medians?
You can also try the method suggested in http://www.ncbi.nlm.nih.gov/pubmed/12243307 (Bonett, Price; 2002) as a simpler (at least computationally, I think) alternative. Good question, by the way. | How to construct a 95% confidence interval of the difference between medians?
You can also try the method suggested in http://www.ncbi.nlm.nih.gov/pubmed/12243307 (Bonett, Price; 2002) as a simpler (at least computationally, I think) alternative. Good question, by the way. |
9,693 | Jaynes' $A_p$ distribution | The main point of $A_p$ theory is to describe how cumulative knowledge changes the probability distributions of new observations, given a model.
Starting from a full ignorance model, consistent observations increase the strength of a belief (narrow variance of $(A_p|E)$), while ignorance or inconsistency generate large... | Jaynes' $A_p$ distribution | The main point of $A_p$ theory is to describe how cumulative knowledge changes the probability distributions of new observations, given a model.
Starting from a full ignorance model, consistent observ | Jaynes' $A_p$ distribution
The main point of $A_p$ theory is to describe how cumulative knowledge changes the probability distributions of new observations, given a model.
Starting from a full ignorance model, consistent observations increase the strength of a belief (narrow variance of $(A_p|E)$), while ignorance or i... | Jaynes' $A_p$ distribution
The main point of $A_p$ theory is to describe how cumulative knowledge changes the probability distributions of new observations, given a model.
Starting from a full ignorance model, consistent observ |
9,694 | What R packages do you find most useful in your daily work? | Please see link:
TOP 100 R PACKAGES FOR 2013 (JAN-MAY)
http://www.r-statistics.com/2013/06/top-100-r-packages-for-2013-jan-may/ | What R packages do you find most useful in your daily work? | Please see link:
TOP 100 R PACKAGES FOR 2013 (JAN-MAY)
http://www.r-statistics.com/2013/06/top-100-r-packages-for-2013-jan-may/ | What R packages do you find most useful in your daily work?
Please see link:
TOP 100 R PACKAGES FOR 2013 (JAN-MAY)
http://www.r-statistics.com/2013/06/top-100-r-packages-for-2013-jan-may/ | What R packages do you find most useful in your daily work?
Please see link:
TOP 100 R PACKAGES FOR 2013 (JAN-MAY)
http://www.r-statistics.com/2013/06/top-100-r-packages-for-2013-jan-may/ |
9,695 | What R packages do you find most useful in your daily work? | I use plyr and ggplot2 the most on a daily basis.
I also rely heavily on time series packages; most especially, the zoo package. | What R packages do you find most useful in your daily work? | I use plyr and ggplot2 the most on a daily basis.
I also rely heavily on time series packages; most especially, the zoo package. | What R packages do you find most useful in your daily work?
I use plyr and ggplot2 the most on a daily basis.
I also rely heavily on time series packages; most especially, the zoo package. | What R packages do you find most useful in your daily work?
I use plyr and ggplot2 the most on a daily basis.
I also rely heavily on time series packages; most especially, the zoo package. |
9,696 | What R packages do you find most useful in your daily work? | In a narrow sense, R Core has a recommendation: the "recommended" packages.
Everything else depends on your data analysis tasks at hand, and I'd recommend the Task Views at CRAN. | What R packages do you find most useful in your daily work? | In a narrow sense, R Core has a recommendation: the "recommended" packages.
Everything else depends on your data analysis tasks at hand, and I'd recommend the Task Views at CRAN. | What R packages do you find most useful in your daily work?
In a narrow sense, R Core has a recommendation: the "recommended" packages.
Everything else depends on your data analysis tasks at hand, and I'd recommend the Task Views at CRAN. | What R packages do you find most useful in your daily work?
In a narrow sense, R Core has a recommendation: the "recommended" packages.
Everything else depends on your data analysis tasks at hand, and I'd recommend the Task Views at CRAN. |
9,697 | What R packages do you find most useful in your daily work? | I use the xtable package. The xtable package turns tables produced by R (in particular, the tables displaying the anova results) into LaTeX tables, to be included in an article. | What R packages do you find most useful in your daily work? | I use the xtable package. The xtable package turns tables produced by R (in particular, the tables displaying the anova results) into LaTeX tables, to be included in an article. | What R packages do you find most useful in your daily work?
I use the xtable package. The xtable package turns tables produced by R (in particular, the tables displaying the anova results) into LaTeX tables, to be included in an article. | What R packages do you find most useful in your daily work?
I use the xtable package. The xtable package turns tables produced by R (in particular, the tables displaying the anova results) into LaTeX tables, to be included in an article. |
9,698 | What R packages do you find most useful in your daily work? | multicore is quite nice for tool for making faster scripts faster.
cacheSweave saves a lot of time when using Sweave. | What R packages do you find most useful in your daily work? | multicore is quite nice for tool for making faster scripts faster.
cacheSweave saves a lot of time when using Sweave. | What R packages do you find most useful in your daily work?
multicore is quite nice for tool for making faster scripts faster.
cacheSweave saves a lot of time when using Sweave. | What R packages do you find most useful in your daily work?
multicore is quite nice for tool for making faster scripts faster.
cacheSweave saves a lot of time when using Sweave. |
9,699 | What R packages do you find most useful in your daily work? | ggplot2 - hands down best visualization for R.
RMySQL/RSQLite/RODBC - for connecting to a databases
sqldf - manipulate data.frames with SQL queries
Hmisc/rms - packages from Frank Harrell containing convenient miscellaneous functions and nice functions for regression analyses.
GenABEL - nice package for genome-wide ass... | What R packages do you find most useful in your daily work? | ggplot2 - hands down best visualization for R.
RMySQL/RSQLite/RODBC - for connecting to a databases
sqldf - manipulate data.frames with SQL queries
Hmisc/rms - packages from Frank Harrell containing c | What R packages do you find most useful in your daily work?
ggplot2 - hands down best visualization for R.
RMySQL/RSQLite/RODBC - for connecting to a databases
sqldf - manipulate data.frames with SQL queries
Hmisc/rms - packages from Frank Harrell containing convenient miscellaneous functions and nice functions for reg... | What R packages do you find most useful in your daily work?
ggplot2 - hands down best visualization for R.
RMySQL/RSQLite/RODBC - for connecting to a databases
sqldf - manipulate data.frames with SQL queries
Hmisc/rms - packages from Frank Harrell containing c |
9,700 | What R packages do you find most useful in your daily work? | data.table is my favorite now! Very look forward to the new version with the more wishlist implemented. | What R packages do you find most useful in your daily work? | data.table is my favorite now! Very look forward to the new version with the more wishlist implemented. | What R packages do you find most useful in your daily work?
data.table is my favorite now! Very look forward to the new version with the more wishlist implemented. | What R packages do you find most useful in your daily work?
data.table is my favorite now! Very look forward to the new version with the more wishlist implemented. |
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