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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7,701 | The relationship between the gamma distribution and the normal distribution | Let us address the question posed, This is all somewhat mysterious to me. Is the normal distribution fundamental to the derivation of the gamma distribution...? No mystery really, it is simply that the normal distribution and the gamma distribution are members, among others of the exponential family of distributions, w... | The relationship between the gamma distribution and the normal distribution | Let us address the question posed, This is all somewhat mysterious to me. Is the normal distribution fundamental to the derivation of the gamma distribution...? No mystery really, it is simply that th | The relationship between the gamma distribution and the normal distribution
Let us address the question posed, This is all somewhat mysterious to me. Is the normal distribution fundamental to the derivation of the gamma distribution...? No mystery really, it is simply that the normal distribution and the gamma distribu... | The relationship between the gamma distribution and the normal distribution
Let us address the question posed, This is all somewhat mysterious to me. Is the normal distribution fundamental to the derivation of the gamma distribution...? No mystery really, it is simply that th |
7,702 | The relationship between the gamma distribution and the normal distribution | The derivation of the chi-squared distribution from the normal distribution is much analogous to the derivation of the gamma distribution from the exponential distribution.
We should be able to generalize this:
If the $X_i$ are independent variables from a generalized normal distribution with power coefficient $m$ th... | The relationship between the gamma distribution and the normal distribution | The derivation of the chi-squared distribution from the normal distribution is much analogous to the derivation of the gamma distribution from the exponential distribution.
We should be able to gener | The relationship between the gamma distribution and the normal distribution
The derivation of the chi-squared distribution from the normal distribution is much analogous to the derivation of the gamma distribution from the exponential distribution.
We should be able to generalize this:
If the $X_i$ are independent va... | The relationship between the gamma distribution and the normal distribution
The derivation of the chi-squared distribution from the normal distribution is much analogous to the derivation of the gamma distribution from the exponential distribution.
We should be able to gener |
7,703 | What is the difference between dropout and drop connect? | DropOut and DropConnect are both methods intended to prevent "co-adaptation" of units in a neural network. In other words, we want units to independently extract features from their inputs instead of relying on other neurons to do so.
Suppose we have a multilayered feedforward network like this one (the topology doesn... | What is the difference between dropout and drop connect? | DropOut and DropConnect are both methods intended to prevent "co-adaptation" of units in a neural network. In other words, we want units to independently extract features from their inputs instead of | What is the difference between dropout and drop connect?
DropOut and DropConnect are both methods intended to prevent "co-adaptation" of units in a neural network. In other words, we want units to independently extract features from their inputs instead of relying on other neurons to do so.
Suppose we have a multilaye... | What is the difference between dropout and drop connect?
DropOut and DropConnect are both methods intended to prevent "co-adaptation" of units in a neural network. In other words, we want units to independently extract features from their inputs instead of |
7,704 | What is the difference between dropout and drop connect? | Yes, but they are slightly different in terms of how the weights are dropped.
These are the formulas of DropConnect (left) and dropout (right).
So dropout applies a mask to the activations, while DropConnect applies a mask to the weights.
The DropConnect paper says that it is a generalization of dropout in the sense... | What is the difference between dropout and drop connect? | Yes, but they are slightly different in terms of how the weights are dropped.
These are the formulas of DropConnect (left) and dropout (right).
So dropout applies a mask to the activations, while Dr | What is the difference between dropout and drop connect?
Yes, but they are slightly different in terms of how the weights are dropped.
These are the formulas of DropConnect (left) and dropout (right).
So dropout applies a mask to the activations, while DropConnect applies a mask to the weights.
The DropConnect paper... | What is the difference between dropout and drop connect?
Yes, but they are slightly different in terms of how the weights are dropped.
These are the formulas of DropConnect (left) and dropout (right).
So dropout applies a mask to the activations, while Dr |
7,705 | What is the difference between dropout and drop connect? | Based on what I saw on Tensorflow 2.5. From the workflow tf.keras.layer.Dropout to tf.python.ops.nn_ops.py function dropout and dropout_v2 (Line 5059 -> 5241), The code does not adjust the shape of the input layer but to mask and do matmul instead with a mask of 0 and 1 to gain speed of continuous vector/matrix/tensor,... | What is the difference between dropout and drop connect? | Based on what I saw on Tensorflow 2.5. From the workflow tf.keras.layer.Dropout to tf.python.ops.nn_ops.py function dropout and dropout_v2 (Line 5059 -> 5241), The code does not adjust the shape of th | What is the difference between dropout and drop connect?
Based on what I saw on Tensorflow 2.5. From the workflow tf.keras.layer.Dropout to tf.python.ops.nn_ops.py function dropout and dropout_v2 (Line 5059 -> 5241), The code does not adjust the shape of the input layer but to mask and do matmul instead with a mask of ... | What is the difference between dropout and drop connect?
Based on what I saw on Tensorflow 2.5. From the workflow tf.keras.layer.Dropout to tf.python.ops.nn_ops.py function dropout and dropout_v2 (Line 5059 -> 5241), The code does not adjust the shape of th |
7,706 | Is whitening always good? | Pre-whitening is a generalization of feature normalization, which makes the input independent by transforming it against a transformed input covariance matrix. I can't see why this may be a bad thing.
However, a quick search revealed "The Feasibility of Data Whitening to Improve Performance of Weather Radar" (pdf) whic... | Is whitening always good? | Pre-whitening is a generalization of feature normalization, which makes the input independent by transforming it against a transformed input covariance matrix. I can't see why this may be a bad thing. | Is whitening always good?
Pre-whitening is a generalization of feature normalization, which makes the input independent by transforming it against a transformed input covariance matrix. I can't see why this may be a bad thing.
However, a quick search revealed "The Feasibility of Data Whitening to Improve Performance of... | Is whitening always good?
Pre-whitening is a generalization of feature normalization, which makes the input independent by transforming it against a transformed input covariance matrix. I can't see why this may be a bad thing. |
7,707 | Is whitening always good? | Firstly, I think that de-correlating and whitening are two separate procedures.
In order to de-correlate the data, we need to transform it so that the transformed data will
have a diagonal covariance matrix. This transform can be found by solving the eigenvalue
problem. We find the eigenvectors and associated eigenval... | Is whitening always good? | Firstly, I think that de-correlating and whitening are two separate procedures.
In order to de-correlate the data, we need to transform it so that the transformed data will
have a diagonal covariance | Is whitening always good?
Firstly, I think that de-correlating and whitening are two separate procedures.
In order to de-correlate the data, we need to transform it so that the transformed data will
have a diagonal covariance matrix. This transform can be found by solving the eigenvalue
problem. We find the eigenvecto... | Is whitening always good?
Firstly, I think that de-correlating and whitening are two separate procedures.
In order to de-correlate the data, we need to transform it so that the transformed data will
have a diagonal covariance |
7,708 | Is whitening always good? | From http://cs231n.github.io/neural-networks-2/
One weakness of this transformation is that it can greatly exaggerate the noise in the data, since it stretches all dimensions (including the irrelevant dimensions of tiny variance that are mostly noise) to be of equal size in the input. This can in practice be mitigated... | Is whitening always good? | From http://cs231n.github.io/neural-networks-2/
One weakness of this transformation is that it can greatly exaggerate the noise in the data, since it stretches all dimensions (including the irrelevan | Is whitening always good?
From http://cs231n.github.io/neural-networks-2/
One weakness of this transformation is that it can greatly exaggerate the noise in the data, since it stretches all dimensions (including the irrelevant dimensions of tiny variance that are mostly noise) to be of equal size in the input. This ca... | Is whitening always good?
From http://cs231n.github.io/neural-networks-2/
One weakness of this transformation is that it can greatly exaggerate the noise in the data, since it stretches all dimensions (including the irrelevan |
7,709 | What's the difference between "deep learning" and multilevel/hierarchical modeling? | Similarity
Fundamentally both types of algorithms were developed to answer one general question in machine learning applications:
Given predictors (factors) $x_1, x_2, \ldots, x_p$ - how to incorporate the interactions between this factors in order to increase the performance?
One way is to simply introduce new predi... | What's the difference between "deep learning" and multilevel/hierarchical modeling? | Similarity
Fundamentally both types of algorithms were developed to answer one general question in machine learning applications:
Given predictors (factors) $x_1, x_2, \ldots, x_p$ - how to incorpora | What's the difference between "deep learning" and multilevel/hierarchical modeling?
Similarity
Fundamentally both types of algorithms were developed to answer one general question in machine learning applications:
Given predictors (factors) $x_1, x_2, \ldots, x_p$ - how to incorporate the interactions between this fac... | What's the difference between "deep learning" and multilevel/hierarchical modeling?
Similarity
Fundamentally both types of algorithms were developed to answer one general question in machine learning applications:
Given predictors (factors) $x_1, x_2, \ldots, x_p$ - how to incorpora |
7,710 | What's the difference between "deep learning" and multilevel/hierarchical modeling? | While this question/answer has been out there for a bit, I hought it might be helpful to clarify a few points in the answer. First, the phrase raised as a major distinction between hierarchical methods and deep neural networks 'This network is fixed.' is incorrect. Hierarchical methods are no more 'fixed' than the alte... | What's the difference between "deep learning" and multilevel/hierarchical modeling? | While this question/answer has been out there for a bit, I hought it might be helpful to clarify a few points in the answer. First, the phrase raised as a major distinction between hierarchical method | What's the difference between "deep learning" and multilevel/hierarchical modeling?
While this question/answer has been out there for a bit, I hought it might be helpful to clarify a few points in the answer. First, the phrase raised as a major distinction between hierarchical methods and deep neural networks 'This net... | What's the difference between "deep learning" and multilevel/hierarchical modeling?
While this question/answer has been out there for a bit, I hought it might be helpful to clarify a few points in the answer. First, the phrase raised as a major distinction between hierarchical method |
7,711 | Performing a statistical test after visualizing data - data dredging? | Briefly disagreeing with/giving a counterpoint to @ingolifs's answer: yes, visualizing your data is essential. But visualizing before deciding on the analysis leads you into Gelman and Loken's garden of forking paths. This is not the same as data-dredging or p-hacking, partly through intent (the GoFP is typically well-... | Performing a statistical test after visualizing data - data dredging? | Briefly disagreeing with/giving a counterpoint to @ingolifs's answer: yes, visualizing your data is essential. But visualizing before deciding on the analysis leads you into Gelman and Loken's garden | Performing a statistical test after visualizing data - data dredging?
Briefly disagreeing with/giving a counterpoint to @ingolifs's answer: yes, visualizing your data is essential. But visualizing before deciding on the analysis leads you into Gelman and Loken's garden of forking paths. This is not the same as data-dre... | Performing a statistical test after visualizing data - data dredging?
Briefly disagreeing with/giving a counterpoint to @ingolifs's answer: yes, visualizing your data is essential. But visualizing before deciding on the analysis leads you into Gelman and Loken's garden |
7,712 | Performing a statistical test after visualizing data - data dredging? | Visualising the data is an indispensable part of analysis and one of the first things you should do with an unfamiliar data set. A quick eyeball of the data can inform the steps to take next. Indeed, it should be fairly obvious by looking at the graph that the means are different, and I'm not sure why a T-test was nece... | Performing a statistical test after visualizing data - data dredging? | Visualising the data is an indispensable part of analysis and one of the first things you should do with an unfamiliar data set. A quick eyeball of the data can inform the steps to take next. Indeed, | Performing a statistical test after visualizing data - data dredging?
Visualising the data is an indispensable part of analysis and one of the first things you should do with an unfamiliar data set. A quick eyeball of the data can inform the steps to take next. Indeed, it should be fairly obvious by looking at the grap... | Performing a statistical test after visualizing data - data dredging?
Visualising the data is an indispensable part of analysis and one of the first things you should do with an unfamiliar data set. A quick eyeball of the data can inform the steps to take next. Indeed, |
7,713 | Does a sample version of the one-sided Chebyshev inequality exist? | Yes, we can get an analogous result using the sample mean and variance, with perhaps, a couple slight surprises emerging in the process.
First, we need to refine the question statement just a little bit and set out a few assumptions. Importantly, it should be clear that we cannot hope to replace the population variance... | Does a sample version of the one-sided Chebyshev inequality exist? | Yes, we can get an analogous result using the sample mean and variance, with perhaps, a couple slight surprises emerging in the process.
First, we need to refine the question statement just a little b | Does a sample version of the one-sided Chebyshev inequality exist?
Yes, we can get an analogous result using the sample mean and variance, with perhaps, a couple slight surprises emerging in the process.
First, we need to refine the question statement just a little bit and set out a few assumptions. Importantly, it sho... | Does a sample version of the one-sided Chebyshev inequality exist?
Yes, we can get an analogous result using the sample mean and variance, with perhaps, a couple slight surprises emerging in the process.
First, we need to refine the question statement just a little b |
7,714 | Does a sample version of the one-sided Chebyshev inequality exist? | This is just a complement to @cardinal 's ingenious answer. Samuelson Inequality, states that, for a sample of size $n$, when we have at least three distinct values of the realized $x_i$'s, it holds that
$$x_i-\bar x < s\sqrt{n-1},\;\; i=1,...n$$
where $s$ is calculated without the bias correction, $s= \left (\frac 1n\... | Does a sample version of the one-sided Chebyshev inequality exist? | This is just a complement to @cardinal 's ingenious answer. Samuelson Inequality, states that, for a sample of size $n$, when we have at least three distinct values of the realized $x_i$'s, it holds t | Does a sample version of the one-sided Chebyshev inequality exist?
This is just a complement to @cardinal 's ingenious answer. Samuelson Inequality, states that, for a sample of size $n$, when we have at least three distinct values of the realized $x_i$'s, it holds that
$$x_i-\bar x < s\sqrt{n-1},\;\; i=1,...n$$
where ... | Does a sample version of the one-sided Chebyshev inequality exist?
This is just a complement to @cardinal 's ingenious answer. Samuelson Inequality, states that, for a sample of size $n$, when we have at least three distinct values of the realized $x_i$'s, it holds t |
7,715 | Does a sample version of the one-sided Chebyshev inequality exist? | I have attempted to apply @cardinal's equation to my permutation test to determine an upper-bound for its p-value. I have 1 unpermuted dataset $y$ and and $n$ permuted datasets $x_i$. I define some testing function $F$ and apply it to my datasets as follows: $F^{orig} = F(y)$ and $F^{perm}_i = F(x_i)$. Then I apply the... | Does a sample version of the one-sided Chebyshev inequality exist? | I have attempted to apply @cardinal's equation to my permutation test to determine an upper-bound for its p-value. I have 1 unpermuted dataset $y$ and and $n$ permuted datasets $x_i$. I define some te | Does a sample version of the one-sided Chebyshev inequality exist?
I have attempted to apply @cardinal's equation to my permutation test to determine an upper-bound for its p-value. I have 1 unpermuted dataset $y$ and and $n$ permuted datasets $x_i$. I define some testing function $F$ and apply it to my datasets as fol... | Does a sample version of the one-sided Chebyshev inequality exist?
I have attempted to apply @cardinal's equation to my permutation test to determine an upper-bound for its p-value. I have 1 unpermuted dataset $y$ and and $n$ permuted datasets $x_i$. I define some te |
7,716 | Why could centering independent variables change the main effects with moderation? | In models with no interaction terms (that is, with no terms that are constructed as the product of other terms), each variable's regression coefficient is the slope of the regression surface in the direction of that variable. It is constant, regardless of the values of the variables, and therefore can be said to measur... | Why could centering independent variables change the main effects with moderation? | In models with no interaction terms (that is, with no terms that are constructed as the product of other terms), each variable's regression coefficient is the slope of the regression surface in the di | Why could centering independent variables change the main effects with moderation?
In models with no interaction terms (that is, with no terms that are constructed as the product of other terms), each variable's regression coefficient is the slope of the regression surface in the direction of that variable. It is const... | Why could centering independent variables change the main effects with moderation?
In models with no interaction terms (that is, with no terms that are constructed as the product of other terms), each variable's regression coefficient is the slope of the regression surface in the di |
7,717 | Why could centering independent variables change the main effects with moderation? | That is because in any regression involving more than one predictor, the $\beta$s are partial coefficients; they are interpreted as the predicted change in the dependent variable for each 1-unit increase in a predictor, holding all other predictors constant.
In a regression involving interaction terms, for example $y=\... | Why could centering independent variables change the main effects with moderation? | That is because in any regression involving more than one predictor, the $\beta$s are partial coefficients; they are interpreted as the predicted change in the dependent variable for each 1-unit incre | Why could centering independent variables change the main effects with moderation?
That is because in any regression involving more than one predictor, the $\beta$s are partial coefficients; they are interpreted as the predicted change in the dependent variable for each 1-unit increase in a predictor, holding all other... | Why could centering independent variables change the main effects with moderation?
That is because in any regression involving more than one predictor, the $\beta$s are partial coefficients; they are interpreted as the predicted change in the dependent variable for each 1-unit incre |
7,718 | Why could centering independent variables change the main effects with moderation? | I have been going crazy with the same question, but i finally found the solution to your and my problem.
IT IS ALL ABOUT HOW YOU CALCULATE YOUR CENTERED VARIABLES. Two options are available:
1. MEAN - INDIVIDUAL VARIABLES
2. INDIVIDUAL VARIABLES - MEAN
You probably calculated your centered variables as (individual ... | Why could centering independent variables change the main effects with moderation? | I have been going crazy with the same question, but i finally found the solution to your and my problem.
IT IS ALL ABOUT HOW YOU CALCULATE YOUR CENTERED VARIABLES. Two options are available:
1. MEAN | Why could centering independent variables change the main effects with moderation?
I have been going crazy with the same question, but i finally found the solution to your and my problem.
IT IS ALL ABOUT HOW YOU CALCULATE YOUR CENTERED VARIABLES. Two options are available:
1. MEAN - INDIVIDUAL VARIABLES
2. INDIVIDU... | Why could centering independent variables change the main effects with moderation?
I have been going crazy with the same question, but i finally found the solution to your and my problem.
IT IS ALL ABOUT HOW YOU CALCULATE YOUR CENTERED VARIABLES. Two options are available:
1. MEAN |
7,719 | How to plot decision boundary of a k-nearest neighbor classifier from Elements of Statistical Learning? | To reproduce this figure, you need to have the ElemStatLearn package installed on you system. The artificial dataset was generated with mixture.example() as pointed out by @StasK.
library(ElemStatLearn)
require(class)
x <- mixture.example$x
g <- mixture.example$y
xnew <- mixture.example$xnew
mod15 <- knn(x, xnew, g, k=... | How to plot decision boundary of a k-nearest neighbor classifier from Elements of Statistical Learni | To reproduce this figure, you need to have the ElemStatLearn package installed on you system. The artificial dataset was generated with mixture.example() as pointed out by @StasK.
library(ElemStatLear | How to plot decision boundary of a k-nearest neighbor classifier from Elements of Statistical Learning?
To reproduce this figure, you need to have the ElemStatLearn package installed on you system. The artificial dataset was generated with mixture.example() as pointed out by @StasK.
library(ElemStatLearn)
require(class... | How to plot decision boundary of a k-nearest neighbor classifier from Elements of Statistical Learni
To reproduce this figure, you need to have the ElemStatLearn package installed on you system. The artificial dataset was generated with mixture.example() as pointed out by @StasK.
library(ElemStatLear |
7,720 | How to plot decision boundary of a k-nearest neighbor classifier from Elements of Statistical Learning? | I'm self-learning ESL and trying to work through all examples provided in the book. I just did this and you can check the R code below:
library(MASS)
# set the seed to reproduce data generation in the future
seed <- 123456
set.seed(seed)
# generate two classes means
Sigma <- matrix(c(1,0,0,1),nrow = 2, ncol = 2)
means... | How to plot decision boundary of a k-nearest neighbor classifier from Elements of Statistical Learni | I'm self-learning ESL and trying to work through all examples provided in the book. I just did this and you can check the R code below:
library(MASS)
# set the seed to reproduce data generation in the | How to plot decision boundary of a k-nearest neighbor classifier from Elements of Statistical Learning?
I'm self-learning ESL and trying to work through all examples provided in the book. I just did this and you can check the R code below:
library(MASS)
# set the seed to reproduce data generation in the future
seed <- ... | How to plot decision boundary of a k-nearest neighbor classifier from Elements of Statistical Learni
I'm self-learning ESL and trying to work through all examples provided in the book. I just did this and you can check the R code below:
library(MASS)
# set the seed to reproduce data generation in the |
7,721 | How to derive the likelihood function for binomial distribution for parameter estimation? | In maximum likelihood estimation, you are trying to maximize $nC_x~p^x(1-p)^{n-x}$; however, maximizing this is equivalent to maximizing $p^x(1-p)^{n-x}$ for a fixed $x$.
Actually, the likelihood for the gaussian and poisson also do not involve their leading constants, so this case is just like those as w
Addressing O... | How to derive the likelihood function for binomial distribution for parameter estimation? | In maximum likelihood estimation, you are trying to maximize $nC_x~p^x(1-p)^{n-x}$; however, maximizing this is equivalent to maximizing $p^x(1-p)^{n-x}$ for a fixed $x$.
Actually, the likelihood for | How to derive the likelihood function for binomial distribution for parameter estimation?
In maximum likelihood estimation, you are trying to maximize $nC_x~p^x(1-p)^{n-x}$; however, maximizing this is equivalent to maximizing $p^x(1-p)^{n-x}$ for a fixed $x$.
Actually, the likelihood for the gaussian and poisson also ... | How to derive the likelihood function for binomial distribution for parameter estimation?
In maximum likelihood estimation, you are trying to maximize $nC_x~p^x(1-p)^{n-x}$; however, maximizing this is equivalent to maximizing $p^x(1-p)^{n-x}$ for a fixed $x$.
Actually, the likelihood for |
7,722 | How to derive the likelihood function for binomial distribution for parameter estimation? | xi in the product refers to each individual trial. For each individual trial xi can be 0 or 1 and n is equal to 1 always. Therefore, trivially, the binomial coefficient will be equal to 1. Hence, in the product formula for likelihood, product of the binomial coefficients will be 1 and hence there is no nCx in the formu... | How to derive the likelihood function for binomial distribution for parameter estimation? | xi in the product refers to each individual trial. For each individual trial xi can be 0 or 1 and n is equal to 1 always. Therefore, trivially, the binomial coefficient will be equal to 1. Hence, in t | How to derive the likelihood function for binomial distribution for parameter estimation?
xi in the product refers to each individual trial. For each individual trial xi can be 0 or 1 and n is equal to 1 always. Therefore, trivially, the binomial coefficient will be equal to 1. Hence, in the product formula for likelih... | How to derive the likelihood function for binomial distribution for parameter estimation?
xi in the product refers to each individual trial. For each individual trial xi can be 0 or 1 and n is equal to 1 always. Therefore, trivially, the binomial coefficient will be equal to 1. Hence, in t |
7,723 | How to derive the likelihood function for binomial distribution for parameter estimation? | It might help to remember that likelihoods are not probabilities. In other words, there is no need to have them sum to 1 over the sample space. Therefore, to make the math happen more quickly we can remove anything that is not a function of the data or the parameter(s) from the definition of the likelihood function. | How to derive the likelihood function for binomial distribution for parameter estimation? | It might help to remember that likelihoods are not probabilities. In other words, there is no need to have them sum to 1 over the sample space. Therefore, to make the math happen more quickly we can | How to derive the likelihood function for binomial distribution for parameter estimation?
It might help to remember that likelihoods are not probabilities. In other words, there is no need to have them sum to 1 over the sample space. Therefore, to make the math happen more quickly we can remove anything that is not a... | How to derive the likelihood function for binomial distribution for parameter estimation?
It might help to remember that likelihoods are not probabilities. In other words, there is no need to have them sum to 1 over the sample space. Therefore, to make the math happen more quickly we can |
7,724 | How to derive the likelihood function for binomial distribution for parameter estimation? | For each factor in the likelihood (i.e. for each individual) "n" = $1$ and "x" = $0$ or $1$. In this case with ($n=1$) we always have $C_x = 1$. So $n C_x = 1$ for each of the factors making up the likelihood. So the normalization IS there, it is just $1$.
In general, a good check that one has written down the likeliho... | How to derive the likelihood function for binomial distribution for parameter estimation? | For each factor in the likelihood (i.e. for each individual) "n" = $1$ and "x" = $0$ or $1$. In this case with ($n=1$) we always have $C_x = 1$. So $n C_x = 1$ for each of the factors making up the li | How to derive the likelihood function for binomial distribution for parameter estimation?
For each factor in the likelihood (i.e. for each individual) "n" = $1$ and "x" = $0$ or $1$. In this case with ($n=1$) we always have $C_x = 1$. So $n C_x = 1$ for each of the factors making up the likelihood. So the normalization... | How to derive the likelihood function for binomial distribution for parameter estimation?
For each factor in the likelihood (i.e. for each individual) "n" = $1$ and "x" = $0$ or $1$. In this case with ($n=1$) we always have $C_x = 1$. So $n C_x = 1$ for each of the factors making up the li |
7,725 | PCA on correlation or covariance: does PCA on correlation ever make sense? [closed] | I hope these responses to your two questions will calm your concern:
A correlation matrix is a covariance matrix of the standardized (i.e. not just centered but also rescaled) data; that is, a covariance matrix (as if) of another, different dataset. So it is natural and it shouldn't bother you that the results differ.... | PCA on correlation or covariance: does PCA on correlation ever make sense? [closed] | I hope these responses to your two questions will calm your concern:
A correlation matrix is a covariance matrix of the standardized (i.e. not just centered but also rescaled) data; that is, a covari | PCA on correlation or covariance: does PCA on correlation ever make sense? [closed]
I hope these responses to your two questions will calm your concern:
A correlation matrix is a covariance matrix of the standardized (i.e. not just centered but also rescaled) data; that is, a covariance matrix (as if) of another, diff... | PCA on correlation or covariance: does PCA on correlation ever make sense? [closed]
I hope these responses to your two questions will calm your concern:
A correlation matrix is a covariance matrix of the standardized (i.e. not just centered but also rescaled) data; that is, a covari |
7,726 | PCA on correlation or covariance: does PCA on correlation ever make sense? [closed] | Speaking from a practical viewpoint - possibly unpopular here - if you have data measured on different scales, then go with correlation ('UV scaling' if you are a chemometrician), but if the variables are on the same scale and the size of them matters (e.g. with spectroscopic data), then covariance (centering the data ... | PCA on correlation or covariance: does PCA on correlation ever make sense? [closed] | Speaking from a practical viewpoint - possibly unpopular here - if you have data measured on different scales, then go with correlation ('UV scaling' if you are a chemometrician), but if the variables | PCA on correlation or covariance: does PCA on correlation ever make sense? [closed]
Speaking from a practical viewpoint - possibly unpopular here - if you have data measured on different scales, then go with correlation ('UV scaling' if you are a chemometrician), but if the variables are on the same scale and the size ... | PCA on correlation or covariance: does PCA on correlation ever make sense? [closed]
Speaking from a practical viewpoint - possibly unpopular here - if you have data measured on different scales, then go with correlation ('UV scaling' if you are a chemometrician), but if the variables |
7,727 | PCA on correlation or covariance: does PCA on correlation ever make sense? [closed] | I have no time to go into a fuller description of detailed & technical aspects of the experiment I described, and clarifications on wordings (recommending, performance, optimum) would again divert us away from the real issue, which is about what type of input data the PCA can(not) / should (not) be taking. PCA operates... | PCA on correlation or covariance: does PCA on correlation ever make sense? [closed] | I have no time to go into a fuller description of detailed & technical aspects of the experiment I described, and clarifications on wordings (recommending, performance, optimum) would again divert us | PCA on correlation or covariance: does PCA on correlation ever make sense? [closed]
I have no time to go into a fuller description of detailed & technical aspects of the experiment I described, and clarifications on wordings (recommending, performance, optimum) would again divert us away from the real issue, which is a... | PCA on correlation or covariance: does PCA on correlation ever make sense? [closed]
I have no time to go into a fuller description of detailed & technical aspects of the experiment I described, and clarifications on wordings (recommending, performance, optimum) would again divert us |
7,728 | What is the difference between EM and Gradient Ascent? | From:
Xu L and Jordan MI (1996). On Convergence Properties of the EM Algorithm for
Gaussian Mixtures. Neural Computation 2: 129-151.
Abstract:
We show that the EM step in parameter space is obtained from the gradient via a projection matrix P, and we provide an explicit expression for the matrix.
Page 2
In part... | What is the difference between EM and Gradient Ascent? | From:
Xu L and Jordan MI (1996). On Convergence Properties of the EM Algorithm for
Gaussian Mixtures. Neural Computation 2: 129-151.
Abstract:
We show that the EM step in parameter space is obta | What is the difference between EM and Gradient Ascent?
From:
Xu L and Jordan MI (1996). On Convergence Properties of the EM Algorithm for
Gaussian Mixtures. Neural Computation 2: 129-151.
Abstract:
We show that the EM step in parameter space is obtained from the gradient via a projection matrix P, and we provide ... | What is the difference between EM and Gradient Ascent?
From:
Xu L and Jordan MI (1996). On Convergence Properties of the EM Algorithm for
Gaussian Mixtures. Neural Computation 2: 129-151.
Abstract:
We show that the EM step in parameter space is obta |
7,729 | What is the difference between EM and Gradient Ascent? | No, they are not equivalent. In particular, EM convergence is much slower.
If you are interested in an optimization point-of-view on EM, in this paper you will see that EM algorithm is a special case of wider class of algorithms (proximal point algorithms). | What is the difference between EM and Gradient Ascent? | No, they are not equivalent. In particular, EM convergence is much slower.
If you are interested in an optimization point-of-view on EM, in this paper you will see that EM algorithm is a special case | What is the difference between EM and Gradient Ascent?
No, they are not equivalent. In particular, EM convergence is much slower.
If you are interested in an optimization point-of-view on EM, in this paper you will see that EM algorithm is a special case of wider class of algorithms (proximal point algorithms). | What is the difference between EM and Gradient Ascent?
No, they are not equivalent. In particular, EM convergence is much slower.
If you are interested in an optimization point-of-view on EM, in this paper you will see that EM algorithm is a special case |
7,730 | What is the difference between EM and Gradient Ascent? | I wanted to follow up (even though this is some years later) on the OP's second question:
Is there any condition under which they are equivalent?
In fact there is a condition under which they're equivalent.
The first order EM algorithm is gradient descent on the marginal likelihood function.
To parse the implications o... | What is the difference between EM and Gradient Ascent? | I wanted to follow up (even though this is some years later) on the OP's second question:
Is there any condition under which they are equivalent?
In fact there is a condition under which they're equiv | What is the difference between EM and Gradient Ascent?
I wanted to follow up (even though this is some years later) on the OP's second question:
Is there any condition under which they are equivalent?
In fact there is a condition under which they're equivalent.
The first order EM algorithm is gradient descent on the ma... | What is the difference between EM and Gradient Ascent?
I wanted to follow up (even though this is some years later) on the OP's second question:
Is there any condition under which they are equivalent?
In fact there is a condition under which they're equiv |
7,731 | In caret what is the real difference between cv and repeatedcv? | According to the caret manual(see "reference manual"), the parameter repeats only applies when the method is set to repeatedcv, so no repetition is performed when the method is set to cv. So the difference between both methods is indeed that repeatedcv repeats and cv does not.
Aside: Repeating a crossvalidation with e... | In caret what is the real difference between cv and repeatedcv? | According to the caret manual(see "reference manual"), the parameter repeats only applies when the method is set to repeatedcv, so no repetition is performed when the method is set to cv. So the diffe | In caret what is the real difference between cv and repeatedcv?
According to the caret manual(see "reference manual"), the parameter repeats only applies when the method is set to repeatedcv, so no repetition is performed when the method is set to cv. So the difference between both methods is indeed that repeatedcv rep... | In caret what is the real difference between cv and repeatedcv?
According to the caret manual(see "reference manual"), the parameter repeats only applies when the method is set to repeatedcv, so no repetition is performed when the method is set to cv. So the diffe |
7,732 | In caret what is the real difference between cv and repeatedcv? | Admittedly, this is a VERY old post, but based on the code snippets provided by user3466398, the difference is that repeatedcv does exactly that: it repeatedly performs X-fold cross-validation on the training data, i.e. if you specify 5 repeats of 10-fold cross-validation, it will perform 10-fold cross-validation on th... | In caret what is the real difference between cv and repeatedcv? | Admittedly, this is a VERY old post, but based on the code snippets provided by user3466398, the difference is that repeatedcv does exactly that: it repeatedly performs X-fold cross-validation on the | In caret what is the real difference between cv and repeatedcv?
Admittedly, this is a VERY old post, but based on the code snippets provided by user3466398, the difference is that repeatedcv does exactly that: it repeatedly performs X-fold cross-validation on the training data, i.e. if you specify 5 repeats of 10-fold ... | In caret what is the real difference between cv and repeatedcv?
Admittedly, this is a VERY old post, but based on the code snippets provided by user3466398, the difference is that repeatedcv does exactly that: it repeatedly performs X-fold cross-validation on the |
7,733 | In caret what is the real difference between cv and repeatedcv? | The actual code behind these parameters can be found in the selectByFilter.R and createDataPartition.R (formerly createFolds.R) source files in the `caret/R/' folder of the package.
See these files for e.g. here and here (beware these permalinks may eventually point to older version of the code). For convenience the r... | In caret what is the real difference between cv and repeatedcv? | The actual code behind these parameters can be found in the selectByFilter.R and createDataPartition.R (formerly createFolds.R) source files in the `caret/R/' folder of the package.
See these files fo | In caret what is the real difference between cv and repeatedcv?
The actual code behind these parameters can be found in the selectByFilter.R and createDataPartition.R (formerly createFolds.R) source files in the `caret/R/' folder of the package.
See these files for e.g. here and here (beware these permalinks may eventu... | In caret what is the real difference between cv and repeatedcv?
The actual code behind these parameters can be found in the selectByFilter.R and createDataPartition.R (formerly createFolds.R) source files in the `caret/R/' folder of the package.
See these files fo |
7,734 | Why is PCA sensitive to outliers? | One of the reasons is that PCA can be thought as low-rank decomposition of the data that minimizes the sum of $L_2$ norms of the residuals of the decomposition. I.e. if $Y$ is your data ($m$ vectors of $n$ dimensions), and $X$ is the PCA basis ($k$ vectors of $n$ dimensions), then the decomposition will strictly minimi... | Why is PCA sensitive to outliers? | One of the reasons is that PCA can be thought as low-rank decomposition of the data that minimizes the sum of $L_2$ norms of the residuals of the decomposition. I.e. if $Y$ is your data ($m$ vectors o | Why is PCA sensitive to outliers?
One of the reasons is that PCA can be thought as low-rank decomposition of the data that minimizes the sum of $L_2$ norms of the residuals of the decomposition. I.e. if $Y$ is your data ($m$ vectors of $n$ dimensions), and $X$ is the PCA basis ($k$ vectors of $n$ dimensions), then the ... | Why is PCA sensitive to outliers?
One of the reasons is that PCA can be thought as low-rank decomposition of the data that minimizes the sum of $L_2$ norms of the residuals of the decomposition. I.e. if $Y$ is your data ($m$ vectors o |
7,735 | Difference between Bayes network, neural network, decision tree and Petri nets | Wow, what a big question! The short version of the answer is that just because you can represent two models using diagrammatically similar visual representations, doesn't mean they are even remotely related structurally, functionally, or philosophically. I'm not familiar with FCM or NF, but I can speak to the other one... | Difference between Bayes network, neural network, decision tree and Petri nets | Wow, what a big question! The short version of the answer is that just because you can represent two models using diagrammatically similar visual representations, doesn't mean they are even remotely r | Difference between Bayes network, neural network, decision tree and Petri nets
Wow, what a big question! The short version of the answer is that just because you can represent two models using diagrammatically similar visual representations, doesn't mean they are even remotely related structurally, functionally, or phi... | Difference between Bayes network, neural network, decision tree and Petri nets
Wow, what a big question! The short version of the answer is that just because you can represent two models using diagrammatically similar visual representations, doesn't mean they are even remotely r |
7,736 | Difference between Bayes network, neural network, decision tree and Petri nets | It is easy to show (see Daphne Koller's course) that Logistic Regression is a restricted version of Conditional Random Fields, which are undirected graphical models, while Bayesian Networks are directed graphical models. Then, Logistic Regression could also be viewed as a single layer perceptron. This is the only link ... | Difference between Bayes network, neural network, decision tree and Petri nets | It is easy to show (see Daphne Koller's course) that Logistic Regression is a restricted version of Conditional Random Fields, which are undirected graphical models, while Bayesian Networks are direct | Difference between Bayes network, neural network, decision tree and Petri nets
It is easy to show (see Daphne Koller's course) that Logistic Regression is a restricted version of Conditional Random Fields, which are undirected graphical models, while Bayesian Networks are directed graphical models. Then, Logistic Regre... | Difference between Bayes network, neural network, decision tree and Petri nets
It is easy to show (see Daphne Koller's course) that Logistic Regression is a restricted version of Conditional Random Fields, which are undirected graphical models, while Bayesian Networks are direct |
7,737 | Difference between Bayes network, neural network, decision tree and Petri nets | First we attempt to state the nature of problem attempted to be solved by these methods. If a problem is straightforward, Polynomial or NP Complete we have ready to plug algorithms that could provide with a deterministic answer, by simple recombination of the axioms along logical rules. However, if that is not the case... | Difference between Bayes network, neural network, decision tree and Petri nets | First we attempt to state the nature of problem attempted to be solved by these methods. If a problem is straightforward, Polynomial or NP Complete we have ready to plug algorithms that could provide | Difference between Bayes network, neural network, decision tree and Petri nets
First we attempt to state the nature of problem attempted to be solved by these methods. If a problem is straightforward, Polynomial or NP Complete we have ready to plug algorithms that could provide with a deterministic answer, by simple re... | Difference between Bayes network, neural network, decision tree and Petri nets
First we attempt to state the nature of problem attempted to be solved by these methods. If a problem is straightforward, Polynomial or NP Complete we have ready to plug algorithms that could provide |
7,738 | Difference between Bayes network, neural network, decision tree and Petri nets | Excellent answer by @David Marx. I have been wondering, what is the difference between Classification/Regression tree and Bayesian network.One builds on entropy to classify an outcome into classes based on different predictors and the other builds a graphical network using conditional independence and probabilistic par... | Difference between Bayes network, neural network, decision tree and Petri nets | Excellent answer by @David Marx. I have been wondering, what is the difference between Classification/Regression tree and Bayesian network.One builds on entropy to classify an outcome into classes bas | Difference between Bayes network, neural network, decision tree and Petri nets
Excellent answer by @David Marx. I have been wondering, what is the difference between Classification/Regression tree and Bayesian network.One builds on entropy to classify an outcome into classes based on different predictors and the other ... | Difference between Bayes network, neural network, decision tree and Petri nets
Excellent answer by @David Marx. I have been wondering, what is the difference between Classification/Regression tree and Bayesian network.One builds on entropy to classify an outcome into classes bas |
7,739 | Difference between Bayes network, neural network, decision tree and Petri nets | Regarding graphical models, Petri Net formalises a system behaviour; in that it sharply differs from the rest of the mentioned models, all of which relate to how a judgement is formed.
Worth to note that most of the cited names designate quite extensive AI concepts, which often coalesce: for example, you may use a Neur... | Difference between Bayes network, neural network, decision tree and Petri nets | Regarding graphical models, Petri Net formalises a system behaviour; in that it sharply differs from the rest of the mentioned models, all of which relate to how a judgement is formed.
Worth to note t | Difference between Bayes network, neural network, decision tree and Petri nets
Regarding graphical models, Petri Net formalises a system behaviour; in that it sharply differs from the rest of the mentioned models, all of which relate to how a judgement is formed.
Worth to note that most of the cited names designate qui... | Difference between Bayes network, neural network, decision tree and Petri nets
Regarding graphical models, Petri Net formalises a system behaviour; in that it sharply differs from the rest of the mentioned models, all of which relate to how a judgement is formed.
Worth to note t |
7,740 | Difference between Bayes network, neural network, decision tree and Petri nets | Its a good question and I've been asking myself the same. There are more than two kinds of neural network, and it seems the previous answer addressed the competitive type, whereas the Bayesian network seems to have similarities to the feed-forward, back-propagation (FFBP) type, and not the competitive type. In fact, I ... | Difference between Bayes network, neural network, decision tree and Petri nets | Its a good question and I've been asking myself the same. There are more than two kinds of neural network, and it seems the previous answer addressed the competitive type, whereas the Bayesian network | Difference between Bayes network, neural network, decision tree and Petri nets
Its a good question and I've been asking myself the same. There are more than two kinds of neural network, and it seems the previous answer addressed the competitive type, whereas the Bayesian network seems to have similarities to the feed-f... | Difference between Bayes network, neural network, decision tree and Petri nets
Its a good question and I've been asking myself the same. There are more than two kinds of neural network, and it seems the previous answer addressed the competitive type, whereas the Bayesian network |
7,741 | How to find confidence intervals for ratings? | Like Karl Broman said in his answer, a Bayesian approach would likely be a lot better than using confidence intervals.
The Problem With Confidence Intervals
Why might using confidence intervals not work too well? One reason is that if you don't have many ratings for an item, then your confidence interval is going to be... | How to find confidence intervals for ratings? | Like Karl Broman said in his answer, a Bayesian approach would likely be a lot better than using confidence intervals.
The Problem With Confidence Intervals
Why might using confidence intervals not wo | How to find confidence intervals for ratings?
Like Karl Broman said in his answer, a Bayesian approach would likely be a lot better than using confidence intervals.
The Problem With Confidence Intervals
Why might using confidence intervals not work too well? One reason is that if you don't have many ratings for an item... | How to find confidence intervals for ratings?
Like Karl Broman said in his answer, a Bayesian approach would likely be a lot better than using confidence intervals.
The Problem With Confidence Intervals
Why might using confidence intervals not wo |
7,742 | How to find confidence intervals for ratings? | This situation cries out for a Bayesian approach. There are simple approaches for Bayesian rankings of ratings here (pay particular to the comments, which are interesting) and here, and then a further commentary on these here. As one of the comments in the first of these links points out:
The Best of BeerAdvocate (B... | How to find confidence intervals for ratings? | This situation cries out for a Bayesian approach. There are simple approaches for Bayesian rankings of ratings here (pay particular to the comments, which are interesting) and here, and then a furthe | How to find confidence intervals for ratings?
This situation cries out for a Bayesian approach. There are simple approaches for Bayesian rankings of ratings here (pay particular to the comments, which are interesting) and here, and then a further commentary on these here. As one of the comments in the first of these ... | How to find confidence intervals for ratings?
This situation cries out for a Bayesian approach. There are simple approaches for Bayesian rankings of ratings here (pay particular to the comments, which are interesting) and here, and then a furthe |
7,743 | How to find confidence intervals for ratings? | The question cites Evan Miller's article "How Not to Sort by Average Rating." Miller has also published an article, "Ranking Items With Star Ratings" that addresses this exact question.
Assume you have 𝐾 possible ratings, indexed by 𝑘, each worth 𝑠𝑘 points. For “star” rating systems, 𝑠𝑘=𝑘. (That is, 1 point, 2 ... | How to find confidence intervals for ratings? | The question cites Evan Miller's article "How Not to Sort by Average Rating." Miller has also published an article, "Ranking Items With Star Ratings" that addresses this exact question.
Assume you ha | How to find confidence intervals for ratings?
The question cites Evan Miller's article "How Not to Sort by Average Rating." Miller has also published an article, "Ranking Items With Star Ratings" that addresses this exact question.
Assume you have 𝐾 possible ratings, indexed by 𝑘, each worth 𝑠𝑘 points. For “star” ... | How to find confidence intervals for ratings?
The question cites Evan Miller's article "How Not to Sort by Average Rating." Miller has also published an article, "Ranking Items With Star Ratings" that addresses this exact question.
Assume you ha |
7,744 | Under which assumptions a regression can be interpreted causally? | I made efforts in this direction and I feel myself in charge to give an answer. I written several answers and questions about this topic. Probably some of them can help you. Among others:
Regression and causality in econometrics
conditional and interventional expectation
linear causal model
Structural equation and caus... | Under which assumptions a regression can be interpreted causally? | I made efforts in this direction and I feel myself in charge to give an answer. I written several answers and questions about this topic. Probably some of them can help you. Among others:
Regression a | Under which assumptions a regression can be interpreted causally?
I made efforts in this direction and I feel myself in charge to give an answer. I written several answers and questions about this topic. Probably some of them can help you. Among others:
Regression and causality in econometrics
conditional and intervent... | Under which assumptions a regression can be interpreted causally?
I made efforts in this direction and I feel myself in charge to give an answer. I written several answers and questions about this topic. Probably some of them can help you. Among others:
Regression a |
7,745 | Under which assumptions a regression can be interpreted causally? | Here's a partial answer for when the underlying model is actually linear. Suppose that the true underlying model is
$$Y = \alpha + \beta X + v.$$
I'm making no assumptions about $v$, though we have that $\beta$ is THE effect of $X$ on $Y$. A linear regression for $\beta$, which we will denote as $\tilde{\beta}$ is simp... | Under which assumptions a regression can be interpreted causally? | Here's a partial answer for when the underlying model is actually linear. Suppose that the true underlying model is
$$Y = \alpha + \beta X + v.$$
I'm making no assumptions about $v$, though we have th | Under which assumptions a regression can be interpreted causally?
Here's a partial answer for when the underlying model is actually linear. Suppose that the true underlying model is
$$Y = \alpha + \beta X + v.$$
I'm making no assumptions about $v$, though we have that $\beta$ is THE effect of $X$ on $Y$. A linear regre... | Under which assumptions a regression can be interpreted causally?
Here's a partial answer for when the underlying model is actually linear. Suppose that the true underlying model is
$$Y = \alpha + \beta X + v.$$
I'm making no assumptions about $v$, though we have th |
7,746 | Under which assumptions a regression can be interpreted causally? | The question is: under which assumptions of the DGP $\text{D}_X(\cdot)$ can we infer the regression (linear or not) represents a causal relationship?
It is well known that experimental data does allow for such interpretation. For what I can read elsewhere, it seems the condition required on the DGP is exogeneity:
$$ \t... | Under which assumptions a regression can be interpreted causally? | The question is: under which assumptions of the DGP $\text{D}_X(\cdot)$ can we infer the regression (linear or not) represents a causal relationship?
It is well known that experimental data does allow | Under which assumptions a regression can be interpreted causally?
The question is: under which assumptions of the DGP $\text{D}_X(\cdot)$ can we infer the regression (linear or not) represents a causal relationship?
It is well known that experimental data does allow for such interpretation. For what I can read elsewher... | Under which assumptions a regression can be interpreted causally?
The question is: under which assumptions of the DGP $\text{D}_X(\cdot)$ can we infer the regression (linear or not) represents a causal relationship?
It is well known that experimental data does allow |
7,747 | Under which assumptions a regression can be interpreted causally? | Short answer:
There is no explicit way of proving causality. All claims of causality must be logically derived, i.e. through common sense (theory). Imagine having an operator (like correlation) which would return causality or non-causality between variables: you would be able to perfectly identify the sources and relat... | Under which assumptions a regression can be interpreted causally? | Short answer:
There is no explicit way of proving causality. All claims of causality must be logically derived, i.e. through common sense (theory). Imagine having an operator (like correlation) which | Under which assumptions a regression can be interpreted causally?
Short answer:
There is no explicit way of proving causality. All claims of causality must be logically derived, i.e. through common sense (theory). Imagine having an operator (like correlation) which would return causality or non-causality between variab... | Under which assumptions a regression can be interpreted causally?
Short answer:
There is no explicit way of proving causality. All claims of causality must be logically derived, i.e. through common sense (theory). Imagine having an operator (like correlation) which |
7,748 | Under which assumptions a regression can be interpreted causally? | Let the true DGP (to be defined below) be
$$y=\mathbf{X}\beta + \mathbf{z}\alpha + \mathbf{v},$$
where $\mathbf{X}$ and $\mathbf{z}$ are regressors, and $\mathbf{z}$ is a $n \times 1$ for simplicity (you can think of it as an index of many variables if that feels restrictive). $\mathbf{v}$ is uncorrelated with $\mathbf... | Under which assumptions a regression can be interpreted causally? | Let the true DGP (to be defined below) be
$$y=\mathbf{X}\beta + \mathbf{z}\alpha + \mathbf{v},$$
where $\mathbf{X}$ and $\mathbf{z}$ are regressors, and $\mathbf{z}$ is a $n \times 1$ for simplicity ( | Under which assumptions a regression can be interpreted causally?
Let the true DGP (to be defined below) be
$$y=\mathbf{X}\beta + \mathbf{z}\alpha + \mathbf{v},$$
where $\mathbf{X}$ and $\mathbf{z}$ are regressors, and $\mathbf{z}$ is a $n \times 1$ for simplicity (you can think of it as an index of many variables if t... | Under which assumptions a regression can be interpreted causally?
Let the true DGP (to be defined below) be
$$y=\mathbf{X}\beta + \mathbf{z}\alpha + \mathbf{v},$$
where $\mathbf{X}$ and $\mathbf{z}$ are regressors, and $\mathbf{z}$ is a $n \times 1$ for simplicity ( |
7,749 | Under which assumptions a regression can be interpreted causally? | Regression is just a series of statistical technique to strengthen causal inferences between two variables of interest by controlling for alternate causal explanations. Even a perfectly linear relationship (r2=1) is meaningless without first establishing the theoretical basis for causality. Classic example being the co... | Under which assumptions a regression can be interpreted causally? | Regression is just a series of statistical technique to strengthen causal inferences between two variables of interest by controlling for alternate causal explanations. Even a perfectly linear relatio | Under which assumptions a regression can be interpreted causally?
Regression is just a series of statistical technique to strengthen causal inferences between two variables of interest by controlling for alternate causal explanations. Even a perfectly linear relationship (r2=1) is meaningless without first establishing... | Under which assumptions a regression can be interpreted causally?
Regression is just a series of statistical technique to strengthen causal inferences between two variables of interest by controlling for alternate causal explanations. Even a perfectly linear relatio |
7,750 | How do decision tree learning algorithms deal with missing values (under the hood) | There are several methods used by various decision trees. Simply ignoring the missing values (like ID3 and other old algorithms does) or treating the missing values as another category (in case of a nominal feature) are not real handling missing values. However those approaches were used in the early stages of decision... | How do decision tree learning algorithms deal with missing values (under the hood) | There are several methods used by various decision trees. Simply ignoring the missing values (like ID3 and other old algorithms does) or treating the missing values as another category (in case of a n | How do decision tree learning algorithms deal with missing values (under the hood)
There are several methods used by various decision trees. Simply ignoring the missing values (like ID3 and other old algorithms does) or treating the missing values as another category (in case of a nominal feature) are not real handling... | How do decision tree learning algorithms deal with missing values (under the hood)
There are several methods used by various decision trees. Simply ignoring the missing values (like ID3 and other old algorithms does) or treating the missing values as another category (in case of a n |
7,751 | What are some useful guidelines for GBM parameters? | The caret package can help you optimize the parameter choice for your problem. The caretTrain vignette shows how to tune the gbm parameters using 10-fold repeated cross-validation - other optimization approaches are available it can all run in parallel using the foreach package. Use vignette("caretTrain", package="car... | What are some useful guidelines for GBM parameters? | The caret package can help you optimize the parameter choice for your problem. The caretTrain vignette shows how to tune the gbm parameters using 10-fold repeated cross-validation - other optimization | What are some useful guidelines for GBM parameters?
The caret package can help you optimize the parameter choice for your problem. The caretTrain vignette shows how to tune the gbm parameters using 10-fold repeated cross-validation - other optimization approaches are available it can all run in parallel using the forea... | What are some useful guidelines for GBM parameters?
The caret package can help you optimize the parameter choice for your problem. The caretTrain vignette shows how to tune the gbm parameters using 10-fold repeated cross-validation - other optimization |
7,752 | What's the difference between the variance and the mean squared error? | The mean squared error as you have written it for OLS is hiding something:
$$\frac{\sum_{i}^{n}(y_i - \hat{y}_i) ^2}{n-2} = \frac{\sum_{i}^{n}\left[y_i - \left(\hat{\beta}_{0} + \hat{\beta}_{x}x_{i}\right)\right] ^2}{n-2}$$
Notice that the numerator sums over a function of both $y$ and $x$, so you lose a degree of free... | What's the difference between the variance and the mean squared error? | The mean squared error as you have written it for OLS is hiding something:
$$\frac{\sum_{i}^{n}(y_i - \hat{y}_i) ^2}{n-2} = \frac{\sum_{i}^{n}\left[y_i - \left(\hat{\beta}_{0} + \hat{\beta}_{x}x_{i}\r | What's the difference between the variance and the mean squared error?
The mean squared error as you have written it for OLS is hiding something:
$$\frac{\sum_{i}^{n}(y_i - \hat{y}_i) ^2}{n-2} = \frac{\sum_{i}^{n}\left[y_i - \left(\hat{\beta}_{0} + \hat{\beta}_{x}x_{i}\right)\right] ^2}{n-2}$$
Notice that the numerator... | What's the difference between the variance and the mean squared error?
The mean squared error as you have written it for OLS is hiding something:
$$\frac{\sum_{i}^{n}(y_i - \hat{y}_i) ^2}{n-2} = \frac{\sum_{i}^{n}\left[y_i - \left(\hat{\beta}_{0} + \hat{\beta}_{x}x_{i}\r |
7,753 | What's the difference between the variance and the mean squared error? | In the variance formula, the sample mean approximates the population mean. The sample mean is calculated for a given sample with $n$ data points. Knowing the sample mean leaves us with only $n-1$ independent data points as the $n$th data point is constrained by the sample mean, so ($n-1$) degrees of freedom (DOF) in th... | What's the difference between the variance and the mean squared error? | In the variance formula, the sample mean approximates the population mean. The sample mean is calculated for a given sample with $n$ data points. Knowing the sample mean leaves us with only $n-1$ inde | What's the difference between the variance and the mean squared error?
In the variance formula, the sample mean approximates the population mean. The sample mean is calculated for a given sample with $n$ data points. Knowing the sample mean leaves us with only $n-1$ independent data points as the $n$th data point is co... | What's the difference between the variance and the mean squared error?
In the variance formula, the sample mean approximates the population mean. The sample mean is calculated for a given sample with $n$ data points. Knowing the sample mean leaves us with only $n-1$ inde |
7,754 | Fisher's Exact Test in contingency tables larger than 2x2 | The only problem with applying Fisher's exact test to tables larger than 2x2 is that the calculations become much more difficult to do. The 2x2 version is the only one which is even feasible by hand, and so I doubt that Fisher ever imagined the test in larger tables because the computations would have been beyond anyt... | Fisher's Exact Test in contingency tables larger than 2x2 | The only problem with applying Fisher's exact test to tables larger than 2x2 is that the calculations become much more difficult to do. The 2x2 version is the only one which is even feasible by hand, | Fisher's Exact Test in contingency tables larger than 2x2
The only problem with applying Fisher's exact test to tables larger than 2x2 is that the calculations become much more difficult to do. The 2x2 version is the only one which is even feasible by hand, and so I doubt that Fisher ever imagined the test in larger t... | Fisher's Exact Test in contingency tables larger than 2x2
The only problem with applying Fisher's exact test to tables larger than 2x2 is that the calculations become much more difficult to do. The 2x2 version is the only one which is even feasible by hand, |
7,755 | Fisher's Exact Test in contingency tables larger than 2x2 | This page in MathWorld
explains how the calculations work. It points out that the test can be defined in a variety of ways:
To compute the P-value of the test,
the tables must be ordered by some
criterion that measures dependence,
and those tables that represent equal
or greater deviation from independence
... | Fisher's Exact Test in contingency tables larger than 2x2 | This page in MathWorld
explains how the calculations work. It points out that the test can be defined in a variety of ways:
To compute the P-value of the test,
the tables must be ordered by some
| Fisher's Exact Test in contingency tables larger than 2x2
This page in MathWorld
explains how the calculations work. It points out that the test can be defined in a variety of ways:
To compute the P-value of the test,
the tables must be ordered by some
criterion that measures dependence,
and those tables that r... | Fisher's Exact Test in contingency tables larger than 2x2
This page in MathWorld
explains how the calculations work. It points out that the test can be defined in a variety of ways:
To compute the P-value of the test,
the tables must be ordered by some
|
7,756 | Fisher's Exact Test in contingency tables larger than 2x2 | If you're looking for other ways to compute Fisher's exact test with larger contingency tables, here is a online calculator for Fisher's exact test for 2x3 contingency tables. Also, here's one for 3x3 contingency tables, and one for 2x4 contingency tables.
Yes, if the expected cell counts are small, it is better to us... | Fisher's Exact Test in contingency tables larger than 2x2 | If you're looking for other ways to compute Fisher's exact test with larger contingency tables, here is a online calculator for Fisher's exact test for 2x3 contingency tables. Also, here's one for 3x | Fisher's Exact Test in contingency tables larger than 2x2
If you're looking for other ways to compute Fisher's exact test with larger contingency tables, here is a online calculator for Fisher's exact test for 2x3 contingency tables. Also, here's one for 3x3 contingency tables, and one for 2x4 contingency tables.
Yes,... | Fisher's Exact Test in contingency tables larger than 2x2
If you're looking for other ways to compute Fisher's exact test with larger contingency tables, here is a online calculator for Fisher's exact test for 2x3 contingency tables. Also, here's one for 3x |
7,757 | Fisher's Exact Test in contingency tables larger than 2x2 | In order to obtain Fisher"s Exact Test in SPSS, use the Statistics = Exact option in Crosstabs. Methods for computing the Exact Tedt for larger tables have been around at least since the 1960"s. The speed of modern microprocessors makes the computation time inconsequential these days. Indeed, it is so easy to run the ... | Fisher's Exact Test in contingency tables larger than 2x2 | In order to obtain Fisher"s Exact Test in SPSS, use the Statistics = Exact option in Crosstabs. Methods for computing the Exact Tedt for larger tables have been around at least since the 1960"s. The | Fisher's Exact Test in contingency tables larger than 2x2
In order to obtain Fisher"s Exact Test in SPSS, use the Statistics = Exact option in Crosstabs. Methods for computing the Exact Tedt for larger tables have been around at least since the 1960"s. The speed of modern microprocessors makes the computation time inc... | Fisher's Exact Test in contingency tables larger than 2x2
In order to obtain Fisher"s Exact Test in SPSS, use the Statistics = Exact option in Crosstabs. Methods for computing the Exact Tedt for larger tables have been around at least since the 1960"s. The |
7,758 | Fisher's Exact Test in contingency tables larger than 2x2 | One important thing to keep in mind here is that Fisher's exact test is typically implemented for contingency tables with fixed margins, i.e. the efficient algorithms utilized in Stata and R involve either generating all tables with fixed margins or sampling all tables with fixed margins.
However, the assumption of fix... | Fisher's Exact Test in contingency tables larger than 2x2 | One important thing to keep in mind here is that Fisher's exact test is typically implemented for contingency tables with fixed margins, i.e. the efficient algorithms utilized in Stata and R involve e | Fisher's Exact Test in contingency tables larger than 2x2
One important thing to keep in mind here is that Fisher's exact test is typically implemented for contingency tables with fixed margins, i.e. the efficient algorithms utilized in Stata and R involve either generating all tables with fixed margins or sampling all... | Fisher's Exact Test in contingency tables larger than 2x2
One important thing to keep in mind here is that Fisher's exact test is typically implemented for contingency tables with fixed margins, i.e. the efficient algorithms utilized in Stata and R involve e |
7,759 | How are Bayesian Priors Decided in Real Life? | There's two big approaches to this problem. Firstly, using relevant past data to somehow "automatically" create a prior (or to somehow include this relevant data into a single model with our new data). This option is often considered attractive, because it "has a certain objectivity to it". Secondly, to ask experts (af... | How are Bayesian Priors Decided in Real Life? | There's two big approaches to this problem. Firstly, using relevant past data to somehow "automatically" create a prior (or to somehow include this relevant data into a single model with our new data) | How are Bayesian Priors Decided in Real Life?
There's two big approaches to this problem. Firstly, using relevant past data to somehow "automatically" create a prior (or to somehow include this relevant data into a single model with our new data). This option is often considered attractive, because it "has a certain ob... | How are Bayesian Priors Decided in Real Life?
There's two big approaches to this problem. Firstly, using relevant past data to somehow "automatically" create a prior (or to somehow include this relevant data into a single model with our new data) |
7,760 | How are Bayesian Priors Decided in Real Life? | OP here, just wanted add some supplementary material and demonstrate the following: a comparison between Frequentist Regression and Bayesian Regression using R
#cool trick to directly bring this data into R
my_data <- data.frame(read.table(header=TRUE,
row.names = 1,
text="
weight height age... | How are Bayesian Priors Decided in Real Life? | OP here, just wanted add some supplementary material and demonstrate the following: a comparison between Frequentist Regression and Bayesian Regression using R
#cool trick to directly bring this data | How are Bayesian Priors Decided in Real Life?
OP here, just wanted add some supplementary material and demonstrate the following: a comparison between Frequentist Regression and Bayesian Regression using R
#cool trick to directly bring this data into R
my_data <- data.frame(read.table(header=TRUE,
row.names = 1,
text=... | How are Bayesian Priors Decided in Real Life?
OP here, just wanted add some supplementary material and demonstrate the following: a comparison between Frequentist Regression and Bayesian Regression using R
#cool trick to directly bring this data |
7,761 | LASSO with interaction terms - is it okay if main effects are shrunk to zero? | One difficulty in answering this question is that it's hard to reconcile LASSO with the idea of a "true" model in most real-world applications, which typically have non-negligible correlations among predictor variables. In that case, as with any variable selection technique, the particular predictors returned with non-... | LASSO with interaction terms - is it okay if main effects are shrunk to zero? | One difficulty in answering this question is that it's hard to reconcile LASSO with the idea of a "true" model in most real-world applications, which typically have non-negligible correlations among p | LASSO with interaction terms - is it okay if main effects are shrunk to zero?
One difficulty in answering this question is that it's hard to reconcile LASSO with the idea of a "true" model in most real-world applications, which typically have non-negligible correlations among predictor variables. In that case, as with ... | LASSO with interaction terms - is it okay if main effects are shrunk to zero?
One difficulty in answering this question is that it's hard to reconcile LASSO with the idea of a "true" model in most real-world applications, which typically have non-negligible correlations among p |
7,762 | LASSO with interaction terms - is it okay if main effects are shrunk to zero? | I am late for a party, but here are few of my thoughts about your problem.
lasso selects what is informative. Lets consider lasso as a method to get the highest predictive performance with the smallest number of features. It is totally fine that in some cases, lasso selects interaction and not main effects. It just me... | LASSO with interaction terms - is it okay if main effects are shrunk to zero? | I am late for a party, but here are few of my thoughts about your problem.
lasso selects what is informative. Lets consider lasso as a method to get the highest predictive performance with the smalle | LASSO with interaction terms - is it okay if main effects are shrunk to zero?
I am late for a party, but here are few of my thoughts about your problem.
lasso selects what is informative. Lets consider lasso as a method to get the highest predictive performance with the smallest number of features. It is totally fine ... | LASSO with interaction terms - is it okay if main effects are shrunk to zero?
I am late for a party, but here are few of my thoughts about your problem.
lasso selects what is informative. Lets consider lasso as a method to get the highest predictive performance with the smalle |
7,763 | LASSO with interaction terms - is it okay if main effects are shrunk to zero? | For the Lasso, if your using it in a predictatory setting then the only thing that matters is how well the cross-validated results are. If you are trying to conduct inference around your effects then read the paper about "Double Lasso". | LASSO with interaction terms - is it okay if main effects are shrunk to zero? | For the Lasso, if your using it in a predictatory setting then the only thing that matters is how well the cross-validated results are. If you are trying to conduct inference around your effects then | LASSO with interaction terms - is it okay if main effects are shrunk to zero?
For the Lasso, if your using it in a predictatory setting then the only thing that matters is how well the cross-validated results are. If you are trying to conduct inference around your effects then read the paper about "Double Lasso". | LASSO with interaction terms - is it okay if main effects are shrunk to zero?
For the Lasso, if your using it in a predictatory setting then the only thing that matters is how well the cross-validated results are. If you are trying to conduct inference around your effects then |
7,764 | LASSO with interaction terms - is it okay if main effects are shrunk to zero? | I have an application where I specifically want small number of main effect to be not penalized. Let Y = X.mainbeta + X.interbeta.inter + eps
a) fit.Y = OLS(X.main,Y). Let tilde.Y = Y - predict(fit.Y,X.main)
b) fit[,j] = OLS(X.main, X.inter[,j]) for j = 1...k. Let tilde.X.inter[,j] = X.inter[,j] - predict(fit.j,X.main)... | LASSO with interaction terms - is it okay if main effects are shrunk to zero? | I have an application where I specifically want small number of main effect to be not penalized. Let Y = X.mainbeta + X.interbeta.inter + eps
a) fit.Y = OLS(X.main,Y). Let tilde.Y = Y - predict(fit.Y, | LASSO with interaction terms - is it okay if main effects are shrunk to zero?
I have an application where I specifically want small number of main effect to be not penalized. Let Y = X.mainbeta + X.interbeta.inter + eps
a) fit.Y = OLS(X.main,Y). Let tilde.Y = Y - predict(fit.Y,X.main)
b) fit[,j] = OLS(X.main, X.inter[,... | LASSO with interaction terms - is it okay if main effects are shrunk to zero?
I have an application where I specifically want small number of main effect to be not penalized. Let Y = X.mainbeta + X.interbeta.inter + eps
a) fit.Y = OLS(X.main,Y). Let tilde.Y = Y - predict(fit.Y, |
7,765 | Why does glmnet use "naive" elastic net from the Zou & Hastie original paper? | I emailed this question to Zou and to Hastie and got the following reply from Hastie (I hope he wouldn't mind me quoting it here):
I think in Zou et al we were worried about the additional bias, but of course rescaling increases the variance.
So it just shifts one along the bias-variance tradeoff curve.
We will soon b... | Why does glmnet use "naive" elastic net from the Zou & Hastie original paper? | I emailed this question to Zou and to Hastie and got the following reply from Hastie (I hope he wouldn't mind me quoting it here):
I think in Zou et al we were worried about the additional bias, but | Why does glmnet use "naive" elastic net from the Zou & Hastie original paper?
I emailed this question to Zou and to Hastie and got the following reply from Hastie (I hope he wouldn't mind me quoting it here):
I think in Zou et al we were worried about the additional bias, but of course rescaling increases the variance... | Why does glmnet use "naive" elastic net from the Zou & Hastie original paper?
I emailed this question to Zou and to Hastie and got the following reply from Hastie (I hope he wouldn't mind me quoting it here):
I think in Zou et al we were worried about the additional bias, but |
7,766 | Feature importance with dummy variables | When working on "feature importance" generally it is helpful to remember that in most cases a regularisation approach is often a good alternative. It will automatically "select the most important features" for the problem at hand.
Now, if we do not want to follow the notion for regularisation (usually within the contex... | Feature importance with dummy variables | When working on "feature importance" generally it is helpful to remember that in most cases a regularisation approach is often a good alternative. It will automatically "select the most important feat | Feature importance with dummy variables
When working on "feature importance" generally it is helpful to remember that in most cases a regularisation approach is often a good alternative. It will automatically "select the most important features" for the problem at hand.
Now, if we do not want to follow the notion for r... | Feature importance with dummy variables
When working on "feature importance" generally it is helpful to remember that in most cases a regularisation approach is often a good alternative. It will automatically "select the most important feat |
7,767 | Feature importance with dummy variables | One approach that you can take in scikit-learn is to use the permutation_importance function on a pipeline that includes the one-hot encoding. If you do this, then the permutation_importance method will be permuting categorical columns before they get one-hot encoded. This approach can be seen in this example on the sc... | Feature importance with dummy variables | One approach that you can take in scikit-learn is to use the permutation_importance function on a pipeline that includes the one-hot encoding. If you do this, then the permutation_importance method wi | Feature importance with dummy variables
One approach that you can take in scikit-learn is to use the permutation_importance function on a pipeline that includes the one-hot encoding. If you do this, then the permutation_importance method will be permuting categorical columns before they get one-hot encoded. This approa... | Feature importance with dummy variables
One approach that you can take in scikit-learn is to use the permutation_importance function on a pipeline that includes the one-hot encoding. If you do this, then the permutation_importance method wi |
7,768 | Feature importance with dummy variables | The question is:
does it make sense to recombine those dummy variable importances into an importance value for a categorical variable by simply summing them?
The short answer:
The simple answer is no. According to the textbook (page 368), the importance of
$$Importance(X_l) = I_{\ell}$$
and
$$(I_{ℓ})^2 = \sum\limits_... | Feature importance with dummy variables | The question is:
does it make sense to recombine those dummy variable importances into an importance value for a categorical variable by simply summing them?
The short answer:
The simple answer is n | Feature importance with dummy variables
The question is:
does it make sense to recombine those dummy variable importances into an importance value for a categorical variable by simply summing them?
The short answer:
The simple answer is no. According to the textbook (page 368), the importance of
$$Importance(X_l) = I... | Feature importance with dummy variables
The question is:
does it make sense to recombine those dummy variable importances into an importance value for a categorical variable by simply summing them?
The short answer:
The simple answer is n |
7,769 | Relation between variational Bayes and EM | Your approach is correct. EM is equivalent to VB under the constraint that the approximate posterior for $\Theta$ is constrained to be a point mass. (This is mentioned without proof on page 337 of Bayesian Data Analysis.) Let $\Theta^*$ be
the unknown location of this point mass:
$$
Q_\Theta(\Theta) = \delta(\Theta... | Relation between variational Bayes and EM | Your approach is correct. EM is equivalent to VB under the constraint that the approximate posterior for $\Theta$ is constrained to be a point mass. (This is mentioned without proof on page 337 of B | Relation between variational Bayes and EM
Your approach is correct. EM is equivalent to VB under the constraint that the approximate posterior for $\Theta$ is constrained to be a point mass. (This is mentioned without proof on page 337 of Bayesian Data Analysis.) Let $\Theta^*$ be
the unknown location of this point... | Relation between variational Bayes and EM
Your approach is correct. EM is equivalent to VB under the constraint that the approximate posterior for $\Theta$ is constrained to be a point mass. (This is mentioned without proof on page 337 of B |
7,770 | Why isn't RANSAC most widely used in statistics? | I think that the key here is the discarding of a large portion of the data in RANSAC.
In most statistical applications, some distributions may have heavy tails, and therefore small sample numbers may skew statistical estimation. Robust estimators solve this by weighing the data differently. RANSAC on the other hand ma... | Why isn't RANSAC most widely used in statistics? | I think that the key here is the discarding of a large portion of the data in RANSAC.
In most statistical applications, some distributions may have heavy tails, and therefore small sample numbers may | Why isn't RANSAC most widely used in statistics?
I think that the key here is the discarding of a large portion of the data in RANSAC.
In most statistical applications, some distributions may have heavy tails, and therefore small sample numbers may skew statistical estimation. Robust estimators solve this by weighing ... | Why isn't RANSAC most widely used in statistics?
I think that the key here is the discarding of a large portion of the data in RANSAC.
In most statistical applications, some distributions may have heavy tails, and therefore small sample numbers may |
7,771 | Why isn't RANSAC most widely used in statistics? | For us, it is just one example of a robust regression -- I believe it is used by statisticians also, but maybe not so wide because it has some better known alternatives. | Why isn't RANSAC most widely used in statistics? | For us, it is just one example of a robust regression -- I believe it is used by statisticians also, but maybe not so wide because it has some better known alternatives. | Why isn't RANSAC most widely used in statistics?
For us, it is just one example of a robust regression -- I believe it is used by statisticians also, but maybe not so wide because it has some better known alternatives. | Why isn't RANSAC most widely used in statistics?
For us, it is just one example of a robust regression -- I believe it is used by statisticians also, but maybe not so wide because it has some better known alternatives. |
7,772 | Why isn't RANSAC most widely used in statistics? | This sounds a lot like bagging which is a frequently used technique. | Why isn't RANSAC most widely used in statistics? | This sounds a lot like bagging which is a frequently used technique. | Why isn't RANSAC most widely used in statistics?
This sounds a lot like bagging which is a frequently used technique. | Why isn't RANSAC most widely used in statistics?
This sounds a lot like bagging which is a frequently used technique. |
7,773 | Why isn't RANSAC most widely used in statistics? | You throw away data with RANSAC, potentially without justifying it, but based on increasing the fit of the model. Throwing away data for increased fit is usually shun as you may loose important data. Removal of outliers without justification is always problematic.
It is ofcourse possible to justify it. E.g. if you kno... | Why isn't RANSAC most widely used in statistics? | You throw away data with RANSAC, potentially without justifying it, but based on increasing the fit of the model. Throwing away data for increased fit is usually shun as you may loose important data. | Why isn't RANSAC most widely used in statistics?
You throw away data with RANSAC, potentially without justifying it, but based on increasing the fit of the model. Throwing away data for increased fit is usually shun as you may loose important data. Removal of outliers without justification is always problematic.
It is... | Why isn't RANSAC most widely used in statistics?
You throw away data with RANSAC, potentially without justifying it, but based on increasing the fit of the model. Throwing away data for increased fit is usually shun as you may loose important data. |
7,774 | Boosting neural networks | In boosting, weak or or unstable classifiers are used as base learners.
This is the case because the aim is to generate decision boundaries that are considerably different. Then, a good base learner is one that is highly biased, in other words, the output remains basically the same even when the training parameters for... | Boosting neural networks | In boosting, weak or or unstable classifiers are used as base learners.
This is the case because the aim is to generate decision boundaries that are considerably different. Then, a good base learner i | Boosting neural networks
In boosting, weak or or unstable classifiers are used as base learners.
This is the case because the aim is to generate decision boundaries that are considerably different. Then, a good base learner is one that is highly biased, in other words, the output remains basically the same even when th... | Boosting neural networks
In boosting, weak or or unstable classifiers are used as base learners.
This is the case because the aim is to generate decision boundaries that are considerably different. Then, a good base learner i |
7,775 | Boosting neural networks | I see this has does not have an accepted answer so I'll give a very heuristic answer. Yes, it is done....e.g. it is available in JMP Pro (probably the best stat package you've never heard of). http://www.jmp.com/support/help/Overview_of_Neural_Networks.shtml
There's a description in in the middle of the page of what ... | Boosting neural networks | I see this has does not have an accepted answer so I'll give a very heuristic answer. Yes, it is done....e.g. it is available in JMP Pro (probably the best stat package you've never heard of). http: | Boosting neural networks
I see this has does not have an accepted answer so I'll give a very heuristic answer. Yes, it is done....e.g. it is available in JMP Pro (probably the best stat package you've never heard of). http://www.jmp.com/support/help/Overview_of_Neural_Networks.shtml
There's a description in in the mi... | Boosting neural networks
I see this has does not have an accepted answer so I'll give a very heuristic answer. Yes, it is done....e.g. it is available in JMP Pro (probably the best stat package you've never heard of). http: |
7,776 | Interpreting negative cosine similarity | Let two vectors $a$ and $b$, the angle $θ$ is obtained by the scalar product and the norm of the vectors :
$$ cos(\theta) = \frac{a \cdot b}{||a|| \cdot ||b||} $$
Since the $cos(\theta)$ value is in the range $[-1,1]$ :
$-1$ value will indicate strongly opposite vectors
$0$ independent (orthogonal) vectors
$1$ simil... | Interpreting negative cosine similarity | Let two vectors $a$ and $b$, the angle $θ$ is obtained by the scalar product and the norm of the vectors :
$$ cos(\theta) = \frac{a \cdot b}{||a|| \cdot ||b||} $$
Since the $cos(\theta)$ value is in | Interpreting negative cosine similarity
Let two vectors $a$ and $b$, the angle $θ$ is obtained by the scalar product and the norm of the vectors :
$$ cos(\theta) = \frac{a \cdot b}{||a|| \cdot ||b||} $$
Since the $cos(\theta)$ value is in the range $[-1,1]$ :
$-1$ value will indicate strongly opposite vectors
$0$ in... | Interpreting negative cosine similarity
Let two vectors $a$ and $b$, the angle $θ$ is obtained by the scalar product and the norm of the vectors :
$$ cos(\theta) = \frac{a \cdot b}{||a|| \cdot ||b||} $$
Since the $cos(\theta)$ value is in |
7,777 | Interpreting negative cosine similarity | Its right that cosine-similarity between frequency vectors cannot be negative as word-counts cannot be negative, but with word-embeddings (such as glove) you can have negative values.
A simplified view of Word-embedding construction is as follows: You assign each word to a random vector in R^d. Next run an optimizer th... | Interpreting negative cosine similarity | Its right that cosine-similarity between frequency vectors cannot be negative as word-counts cannot be negative, but with word-embeddings (such as glove) you can have negative values.
A simplified vie | Interpreting negative cosine similarity
Its right that cosine-similarity between frequency vectors cannot be negative as word-counts cannot be negative, but with word-embeddings (such as glove) you can have negative values.
A simplified view of Word-embedding construction is as follows: You assign each word to a random... | Interpreting negative cosine similarity
Its right that cosine-similarity between frequency vectors cannot be negative as word-counts cannot be negative, but with word-embeddings (such as glove) you can have negative values.
A simplified vie |
7,778 | Interpreting negative cosine similarity | Do not use the absolute values, as the negative sign is not arbitrary. To acquire a cosine value between 0 and 1, you should use the following cosine function:
(R code)
cos.sim <- function(a,b)
{
dot_product = sum(a*b)
anorm = sqrt(sum((a)^2))
bnorm = sqrt(sum((b)^2))
minx =-1
maxx = 1
return(((dot_product... | Interpreting negative cosine similarity | Do not use the absolute values, as the negative sign is not arbitrary. To acquire a cosine value between 0 and 1, you should use the following cosine function:
(R code)
cos.sim <- function(a,b)
{
d | Interpreting negative cosine similarity
Do not use the absolute values, as the negative sign is not arbitrary. To acquire a cosine value between 0 and 1, you should use the following cosine function:
(R code)
cos.sim <- function(a,b)
{
dot_product = sum(a*b)
anorm = sqrt(sum((a)^2))
bnorm = sqrt(sum((b)^2))
mi... | Interpreting negative cosine similarity
Do not use the absolute values, as the negative sign is not arbitrary. To acquire a cosine value between 0 and 1, you should use the following cosine function:
(R code)
cos.sim <- function(a,b)
{
d |
7,779 | Interpreting negative cosine similarity | Cosine similarity is just like Pearson correlation, but without substracting the means. So you can compare the relative strengh of 2 cosine similarities by looking at the absolute values, just like how you would compare the absolute values of 2 Pearson correlations. | Interpreting negative cosine similarity | Cosine similarity is just like Pearson correlation, but without substracting the means. So you can compare the relative strengh of 2 cosine similarities by looking at the absolute values, just like ho | Interpreting negative cosine similarity
Cosine similarity is just like Pearson correlation, but without substracting the means. So you can compare the relative strengh of 2 cosine similarities by looking at the absolute values, just like how you would compare the absolute values of 2 Pearson correlations. | Interpreting negative cosine similarity
Cosine similarity is just like Pearson correlation, but without substracting the means. So you can compare the relative strengh of 2 cosine similarities by looking at the absolute values, just like ho |
7,780 | What is the most accurate way of determining an object's color? | Two things, for starters.
One, definitively do not work in RGB. Your default should be Lab (aka CIE L*a*b*) colorspace. Discard L. From your image it looks like the a coordinate gives you the most information, but you probably should do a principal component analysis on a and b and work along the first (most important)... | What is the most accurate way of determining an object's color? | Two things, for starters.
One, definitively do not work in RGB. Your default should be Lab (aka CIE L*a*b*) colorspace. Discard L. From your image it looks like the a coordinate gives you the most inf | What is the most accurate way of determining an object's color?
Two things, for starters.
One, definitively do not work in RGB. Your default should be Lab (aka CIE L*a*b*) colorspace. Discard L. From your image it looks like the a coordinate gives you the most information, but you probably should do a principal compone... | What is the most accurate way of determining an object's color?
Two things, for starters.
One, definitively do not work in RGB. Your default should be Lab (aka CIE L*a*b*) colorspace. Discard L. From your image it looks like the a coordinate gives you the most inf |
7,781 | What is the most accurate way of determining an object's color? | In the spirit of brainstorming, I'll share some ideas you could try:
Try Hue more? It looks like Hue gave you a pretty good discriminator between silver and copper/gold, though not between copper and gold, at least in the single example you showed here. Have you examined using the Hue in greater detail, to see whethe... | What is the most accurate way of determining an object's color? | In the spirit of brainstorming, I'll share some ideas you could try:
Try Hue more? It looks like Hue gave you a pretty good discriminator between silver and copper/gold, though not between copper and | What is the most accurate way of determining an object's color?
In the spirit of brainstorming, I'll share some ideas you could try:
Try Hue more? It looks like Hue gave you a pretty good discriminator between silver and copper/gold, though not between copper and gold, at least in the single example you showed here. ... | What is the most accurate way of determining an object's color?
In the spirit of brainstorming, I'll share some ideas you could try:
Try Hue more? It looks like Hue gave you a pretty good discriminator between silver and copper/gold, though not between copper and |
7,782 | What is the most accurate way of determining an object's color? | Interesting problem and good work.
Try using median colour values rather than mean. This will be more robust against outlier values due to brightness and saturation. Try using just one of the RGB components instead of all three. Choose the component that best distinguishes the colours. You could try plotting histograms... | What is the most accurate way of determining an object's color? | Interesting problem and good work.
Try using median colour values rather than mean. This will be more robust against outlier values due to brightness and saturation. Try using just one of the RGB comp | What is the most accurate way of determining an object's color?
Interesting problem and good work.
Try using median colour values rather than mean. This will be more robust against outlier values due to brightness and saturation. Try using just one of the RGB components instead of all three. Choose the component that b... | What is the most accurate way of determining an object's color?
Interesting problem and good work.
Try using median colour values rather than mean. This will be more robust against outlier values due to brightness and saturation. Try using just one of the RGB comp |
7,783 | Raw residuals versus standardised residuals versus studentised residuals - what to use when? | This isn't so much an answer as a clarification on terminology. Your question asks about raw, standarized, and studentized residuals. However, this is not the terminology used by most statisticians, though I note your class notes state that it is.
Raw: same as you have it.
Standardized: this is actually the raw resid... | Raw residuals versus standardised residuals versus studentised residuals - what to use when? | This isn't so much an answer as a clarification on terminology. Your question asks about raw, standarized, and studentized residuals. However, this is not the terminology used by most statisticians, | Raw residuals versus standardised residuals versus studentised residuals - what to use when?
This isn't so much an answer as a clarification on terminology. Your question asks about raw, standarized, and studentized residuals. However, this is not the terminology used by most statisticians, though I note your class n... | Raw residuals versus standardised residuals versus studentised residuals - what to use when?
This isn't so much an answer as a clarification on terminology. Your question asks about raw, standarized, and studentized residuals. However, this is not the terminology used by most statisticians, |
7,784 | Raw residuals versus standardised residuals versus studentised residuals - what to use when? | Re: plots,
There is such a thing as overfitting, but overplotting cannot really do much harm, especially at diagnostics stage. A standardized normal probability plot cannot hurt next to your QQ-plot. I find it better to assess the middle of the distribution.
Re: residuals,
I run both standardized and studentized residu... | Raw residuals versus standardised residuals versus studentised residuals - what to use when? | Re: plots,
There is such a thing as overfitting, but overplotting cannot really do much harm, especially at diagnostics stage. A standardized normal probability plot cannot hurt next to your QQ-plot. | Raw residuals versus standardised residuals versus studentised residuals - what to use when?
Re: plots,
There is such a thing as overfitting, but overplotting cannot really do much harm, especially at diagnostics stage. A standardized normal probability plot cannot hurt next to your QQ-plot. I find it better to assess ... | Raw residuals versus standardised residuals versus studentised residuals - what to use when?
Re: plots,
There is such a thing as overfitting, but overplotting cannot really do much harm, especially at diagnostics stage. A standardized normal probability plot cannot hurt next to your QQ-plot. |
7,785 | What problem does oversampling, undersampling, and SMOTE solve? | The problem that these methods are trying to solve is to increase the impact of minority class on cost function. This is because algos trying to fit well the whole dataset and then adapt to majority. Other approach would be to use class weights, and this aporoach in most cases gives better results, since there is no in... | What problem does oversampling, undersampling, and SMOTE solve? | The problem that these methods are trying to solve is to increase the impact of minority class on cost function. This is because algos trying to fit well the whole dataset and then adapt to majority. | What problem does oversampling, undersampling, and SMOTE solve?
The problem that these methods are trying to solve is to increase the impact of minority class on cost function. This is because algos trying to fit well the whole dataset and then adapt to majority. Other approach would be to use class weights, and this a... | What problem does oversampling, undersampling, and SMOTE solve?
The problem that these methods are trying to solve is to increase the impact of minority class on cost function. This is because algos trying to fit well the whole dataset and then adapt to majority. |
7,786 | What problem does oversampling, undersampling, and SMOTE solve? | Some sampling techniques are to adjust for bias (if the population rate is known and different), but I agree with the notion that the unbalanced class is not the problem itself. One major reason comes down to processing performance. If our targeted class, for example, is an extreme rare case at 1:100000, our modeling d... | What problem does oversampling, undersampling, and SMOTE solve? | Some sampling techniques are to adjust for bias (if the population rate is known and different), but I agree with the notion that the unbalanced class is not the problem itself. One major reason comes | What problem does oversampling, undersampling, and SMOTE solve?
Some sampling techniques are to adjust for bias (if the population rate is known and different), but I agree with the notion that the unbalanced class is not the problem itself. One major reason comes down to processing performance. If our targeted class, ... | What problem does oversampling, undersampling, and SMOTE solve?
Some sampling techniques are to adjust for bias (if the population rate is known and different), but I agree with the notion that the unbalanced class is not the problem itself. One major reason comes |
7,787 | What problem does oversampling, undersampling, and SMOTE solve? | I will give you a more extreme example. Consider the case where you have a dataset with 99 data points labeled as positive and only one labeled as negative. During training, your model will realize that if it classifies everything as positive, it will end up getting away with it. One way of fixing this is to oversample... | What problem does oversampling, undersampling, and SMOTE solve? | I will give you a more extreme example. Consider the case where you have a dataset with 99 data points labeled as positive and only one labeled as negative. During training, your model will realize th | What problem does oversampling, undersampling, and SMOTE solve?
I will give you a more extreme example. Consider the case where you have a dataset with 99 data points labeled as positive and only one labeled as negative. During training, your model will realize that if it classifies everything as positive, it will end ... | What problem does oversampling, undersampling, and SMOTE solve?
I will give you a more extreme example. Consider the case where you have a dataset with 99 data points labeled as positive and only one labeled as negative. During training, your model will realize th |
7,788 | What problem does oversampling, undersampling, and SMOTE solve? | I'm going to disagree with the premise that unbalanced data isn't a problem in machine learning. Perhaps less so in regression, but it certainly is in classification.
Imbalanced Data is relevant in Machine Learning applications because of decreased performance of algorithms (the research I am thinking of is specifical... | What problem does oversampling, undersampling, and SMOTE solve? | I'm going to disagree with the premise that unbalanced data isn't a problem in machine learning. Perhaps less so in regression, but it certainly is in classification.
Imbalanced Data is relevant in M | What problem does oversampling, undersampling, and SMOTE solve?
I'm going to disagree with the premise that unbalanced data isn't a problem in machine learning. Perhaps less so in regression, but it certainly is in classification.
Imbalanced Data is relevant in Machine Learning applications because of decreased perfor... | What problem does oversampling, undersampling, and SMOTE solve?
I'm going to disagree with the premise that unbalanced data isn't a problem in machine learning. Perhaps less so in regression, but it certainly is in classification.
Imbalanced Data is relevant in M |
7,789 | What problem does oversampling, undersampling, and SMOTE solve? | There are many technqiues for oversampling and undersampling to overcome the sparsity of minority in imbalanced data anv vice versa.... Yet most of them have consequence on behavior of your model (roughly speaking variance). I personally use an self-devised technique where oversampling and undersampling are done simoul... | What problem does oversampling, undersampling, and SMOTE solve? | There are many technqiues for oversampling and undersampling to overcome the sparsity of minority in imbalanced data anv vice versa.... Yet most of them have consequence on behavior of your model (rou | What problem does oversampling, undersampling, and SMOTE solve?
There are many technqiues for oversampling and undersampling to overcome the sparsity of minority in imbalanced data anv vice versa.... Yet most of them have consequence on behavior of your model (roughly speaking variance). I personally use an self-devise... | What problem does oversampling, undersampling, and SMOTE solve?
There are many technqiues for oversampling and undersampling to overcome the sparsity of minority in imbalanced data anv vice versa.... Yet most of them have consequence on behavior of your model (rou |
7,790 | Intuitive explanation of "Statistical Inference" | Sometimes it's best to explain a concept through a concrete example:
Imagine you grab an apple, take a bite from it and it tastes sweet. Will you conclude based on that bite that the entire apple is sweet? If yes, you will have inferred that the entire apple is sweet based on a single bite from it.
Inference is the pro... | Intuitive explanation of "Statistical Inference" | Sometimes it's best to explain a concept through a concrete example:
Imagine you grab an apple, take a bite from it and it tastes sweet. Will you conclude based on that bite that the entire apple is s | Intuitive explanation of "Statistical Inference"
Sometimes it's best to explain a concept through a concrete example:
Imagine you grab an apple, take a bite from it and it tastes sweet. Will you conclude based on that bite that the entire apple is sweet? If yes, you will have inferred that the entire apple is sweet bas... | Intuitive explanation of "Statistical Inference"
Sometimes it's best to explain a concept through a concrete example:
Imagine you grab an apple, take a bite from it and it tastes sweet. Will you conclude based on that bite that the entire apple is s |
7,791 | Intuitive explanation of "Statistical Inference" | I'm assuming that you're asking in here about statistical inference.
Using the definition from All of Statistics by Larry A. Wasserman:
Statistical inference, or “learning” as it is called in computer
science, is the process of using data to infer the distribution that
generated the data. A typical statistical inferen... | Intuitive explanation of "Statistical Inference" | I'm assuming that you're asking in here about statistical inference.
Using the definition from All of Statistics by Larry A. Wasserman:
Statistical inference, or “learning” as it is called in compute | Intuitive explanation of "Statistical Inference"
I'm assuming that you're asking in here about statistical inference.
Using the definition from All of Statistics by Larry A. Wasserman:
Statistical inference, or “learning” as it is called in computer
science, is the process of using data to infer the distribution that
... | Intuitive explanation of "Statistical Inference"
I'm assuming that you're asking in here about statistical inference.
Using the definition from All of Statistics by Larry A. Wasserman:
Statistical inference, or “learning” as it is called in compute |
7,792 | Intuitive explanation of "Statistical Inference" | Citing E.T.Jaynes, "Probability theory: the logic of science" (a highly recommended read):
By 'inference' we mean simply: deductive reasoning whenever enough information is at hand to permit it; inductive or plausible reasoning when - as is almost invariably the case in real problems - the necessary information is not... | Intuitive explanation of "Statistical Inference" | Citing E.T.Jaynes, "Probability theory: the logic of science" (a highly recommended read):
By 'inference' we mean simply: deductive reasoning whenever enough information is at hand to permit it; indu | Intuitive explanation of "Statistical Inference"
Citing E.T.Jaynes, "Probability theory: the logic of science" (a highly recommended read):
By 'inference' we mean simply: deductive reasoning whenever enough information is at hand to permit it; inductive or plausible reasoning when - as is almost invariably the case in... | Intuitive explanation of "Statistical Inference"
Citing E.T.Jaynes, "Probability theory: the logic of science" (a highly recommended read):
By 'inference' we mean simply: deductive reasoning whenever enough information is at hand to permit it; indu |
7,793 | Intuitive explanation of "Statistical Inference" | Statistical inference is the art of good guessing --- it entails guessing things that are unknown from related things that are known (observed), and giving associated measures of the level of confidence, variability, etc., in your guess. | Intuitive explanation of "Statistical Inference" | Statistical inference is the art of good guessing --- it entails guessing things that are unknown from related things that are known (observed), and giving associated measures of the level of confiden | Intuitive explanation of "Statistical Inference"
Statistical inference is the art of good guessing --- it entails guessing things that are unknown from related things that are known (observed), and giving associated measures of the level of confidence, variability, etc., in your guess. | Intuitive explanation of "Statistical Inference"
Statistical inference is the art of good guessing --- it entails guessing things that are unknown from related things that are known (observed), and giving associated measures of the level of confiden |
7,794 | Intuitive explanation of "Statistical Inference" | Let me try. The broad dictionary definition of inference is as follows:
something that you can find out indirectly from what you already know
And, from a more technical perspective, from The Oxford Dictionary of Statistical Terms by Upton, G., Cook I.,
statistical inference is the process of using data analysis to d... | Intuitive explanation of "Statistical Inference" | Let me try. The broad dictionary definition of inference is as follows:
something that you can find out indirectly from what you already know
And, from a more technical perspective, from The Oxford | Intuitive explanation of "Statistical Inference"
Let me try. The broad dictionary definition of inference is as follows:
something that you can find out indirectly from what you already know
And, from a more technical perspective, from The Oxford Dictionary of Statistical Terms by Upton, G., Cook I.,
statistical inf... | Intuitive explanation of "Statistical Inference"
Let me try. The broad dictionary definition of inference is as follows:
something that you can find out indirectly from what you already know
And, from a more technical perspective, from The Oxford |
7,795 | Intuitive explanation of "Statistical Inference" | I'll try to rephrase Tim's answer since I think it's too technical for a layman.
Inference is the process of extracting (inferring) a general pattern from a particular set of cases. E.g., we have these particular data about soil, fertilizers and yield. What can we say about the general effect of soils and fertilizers o... | Intuitive explanation of "Statistical Inference" | I'll try to rephrase Tim's answer since I think it's too technical for a layman.
Inference is the process of extracting (inferring) a general pattern from a particular set of cases. E.g., we have thes | Intuitive explanation of "Statistical Inference"
I'll try to rephrase Tim's answer since I think it's too technical for a layman.
Inference is the process of extracting (inferring) a general pattern from a particular set of cases. E.g., we have these particular data about soil, fertilizers and yield. What can we say ab... | Intuitive explanation of "Statistical Inference"
I'll try to rephrase Tim's answer since I think it's too technical for a layman.
Inference is the process of extracting (inferring) a general pattern from a particular set of cases. E.g., we have thes |
7,796 | Intuitive explanation of "Statistical Inference" | From the contents of two popular textbooks,
Casella and Berger (1990) -- Statistical Inference
Efron (2006) -- Computer Age Statistical Inference
I think statistical inference simply means mathematical and reasoning activities that try to make sense of data. More specifically, one may discern two approaches -- Bayesian... | Intuitive explanation of "Statistical Inference" | From the contents of two popular textbooks,
Casella and Berger (1990) -- Statistical Inference
Efron (2006) -- Computer Age Statistical Inference
I think statistical inference simply means mathematica | Intuitive explanation of "Statistical Inference"
From the contents of two popular textbooks,
Casella and Berger (1990) -- Statistical Inference
Efron (2006) -- Computer Age Statistical Inference
I think statistical inference simply means mathematical and reasoning activities that try to make sense of data. More specifi... | Intuitive explanation of "Statistical Inference"
From the contents of two popular textbooks,
Casella and Berger (1990) -- Statistical Inference
Efron (2006) -- Computer Age Statistical Inference
I think statistical inference simply means mathematica |
7,797 | Intuitive explanation of "Statistical Inference" | Take this following case for example:
You want to know men's average height in the U.S. How could you proceed with this problem?
In an ideal situation, if you had unlimited time and energy, you can certainly collect the statistics from different resources and compiled them together to figure out the "undisputed truth" ... | Intuitive explanation of "Statistical Inference" | Take this following case for example:
You want to know men's average height in the U.S. How could you proceed with this problem?
In an ideal situation, if you had unlimited time and energy, you can ce | Intuitive explanation of "Statistical Inference"
Take this following case for example:
You want to know men's average height in the U.S. How could you proceed with this problem?
In an ideal situation, if you had unlimited time and energy, you can certainly collect the statistics from different resources and compiled th... | Intuitive explanation of "Statistical Inference"
Take this following case for example:
You want to know men's average height in the U.S. How could you proceed with this problem?
In an ideal situation, if you had unlimited time and energy, you can ce |
7,798 | What's the point of time series analysis? | One main use is forecasting. I have been feeding my family for over a decade now by forecasting how many units of a specific product a supermarket will sell tomorrow, so he can order enough stock, but not too much. There is money in this.
Other forecasting use cases are given in publications like the International Jour... | What's the point of time series analysis? | One main use is forecasting. I have been feeding my family for over a decade now by forecasting how many units of a specific product a supermarket will sell tomorrow, so he can order enough stock, but | What's the point of time series analysis?
One main use is forecasting. I have been feeding my family for over a decade now by forecasting how many units of a specific product a supermarket will sell tomorrow, so he can order enough stock, but not too much. There is money in this.
Other forecasting use cases are given i... | What's the point of time series analysis?
One main use is forecasting. I have been feeding my family for over a decade now by forecasting how many units of a specific product a supermarket will sell tomorrow, so he can order enough stock, but |
7,799 | What's the point of time series analysis? | Goals in TS Analysis from the lesson-slides of M. Dettling:
1) Exploratory Analysis:
Visualization of the properties of the series
time series plot
decomposition into trend/seasonal pattern/random error
correlogram for understanding the dependency structure
2) Modeling:
Fitting a stochastic model to the data that ... | What's the point of time series analysis? | Goals in TS Analysis from the lesson-slides of M. Dettling:
1) Exploratory Analysis:
Visualization of the properties of the series
time series plot
decomposition into trend/seasonal pattern/random e | What's the point of time series analysis?
Goals in TS Analysis from the lesson-slides of M. Dettling:
1) Exploratory Analysis:
Visualization of the properties of the series
time series plot
decomposition into trend/seasonal pattern/random error
correlogram for understanding the dependency structure
2) Modeling:
Fi... | What's the point of time series analysis?
Goals in TS Analysis from the lesson-slides of M. Dettling:
1) Exploratory Analysis:
Visualization of the properties of the series
time series plot
decomposition into trend/seasonal pattern/random e |
7,800 | What's the point of time series analysis? | The easiest way to answer your question is to understand that roughly the data sets are often categorized as cross-sectional, time series and panel. Cross-sectional regression is a go-to tool for the cross-sectional data sets. This is what most people know and refer to with a term regression. Time series regression is ... | What's the point of time series analysis? | The easiest way to answer your question is to understand that roughly the data sets are often categorized as cross-sectional, time series and panel. Cross-sectional regression is a go-to tool for the | What's the point of time series analysis?
The easiest way to answer your question is to understand that roughly the data sets are often categorized as cross-sectional, time series and panel. Cross-sectional regression is a go-to tool for the cross-sectional data sets. This is what most people know and refer to with a t... | What's the point of time series analysis?
The easiest way to answer your question is to understand that roughly the data sets are often categorized as cross-sectional, time series and panel. Cross-sectional regression is a go-to tool for the |
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