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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3,001 | Which activation function for output layer? | Sigmoid and tanh should not be used as activation function for the hidden layer. This is because of the vanishing gradient problem, i.e., if your input is on a higher side (where sigmoid goes flat) then the gradient will be near zero. This will cause very slow or no learning during backpropagation as weights will be up... | Which activation function for output layer? | Sigmoid and tanh should not be used as activation function for the hidden layer. This is because of the vanishing gradient problem, i.e., if your input is on a higher side (where sigmoid goes flat) th | Which activation function for output layer?
Sigmoid and tanh should not be used as activation function for the hidden layer. This is because of the vanishing gradient problem, i.e., if your input is on a higher side (where sigmoid goes flat) then the gradient will be near zero. This will cause very slow or no learning ... | Which activation function for output layer?
Sigmoid and tanh should not be used as activation function for the hidden layer. This is because of the vanishing gradient problem, i.e., if your input is on a higher side (where sigmoid goes flat) th |
3,002 | Which activation function for output layer? | Softmax outputs produce a vector that is non-negative and sums to 1. It's useful when you have mutually exclusive categories ("these images only contain cats or dogs, not both"). You can use softmax if you have $2,3,4,5,...$ mutually exclusive labels.
Using $2,3,4,...$ sigmoid outputs produce a vector where each elemen... | Which activation function for output layer? | Softmax outputs produce a vector that is non-negative and sums to 1. It's useful when you have mutually exclusive categories ("these images only contain cats or dogs, not both"). You can use softmax i | Which activation function for output layer?
Softmax outputs produce a vector that is non-negative and sums to 1. It's useful when you have mutually exclusive categories ("these images only contain cats or dogs, not both"). You can use softmax if you have $2,3,4,5,...$ mutually exclusive labels.
Using $2,3,4,...$ sigmoi... | Which activation function for output layer?
Softmax outputs produce a vector that is non-negative and sums to 1. It's useful when you have mutually exclusive categories ("these images only contain cats or dogs, not both"). You can use softmax i |
3,003 | Which activation function for output layer? | The choice of the activation function for the output layer depends on the constraints of the problem. I will give my answer based on different examples:
Fitting in Supervised Learning: any activation function can be used in this problem. In some cases, the target data would have to be mapped within the image of the ac... | Which activation function for output layer? | The choice of the activation function for the output layer depends on the constraints of the problem. I will give my answer based on different examples:
Fitting in Supervised Learning: any activation | Which activation function for output layer?
The choice of the activation function for the output layer depends on the constraints of the problem. I will give my answer based on different examples:
Fitting in Supervised Learning: any activation function can be used in this problem. In some cases, the target data would ... | Which activation function for output layer?
The choice of the activation function for the output layer depends on the constraints of the problem. I will give my answer based on different examples:
Fitting in Supervised Learning: any activation |
3,004 | Which activation function for output layer? | The reason why different activations exist is because they match different problems. Among a few others are mentioned by you "linear functions, sigmoid functions and softmax functions":
linear is an obvious choice for regression problems where you are predicting unbounded quantities, e.g. stock log returns.
sigmoid ca... | Which activation function for output layer? | The reason why different activations exist is because they match different problems. Among a few others are mentioned by you "linear functions, sigmoid functions and softmax functions":
linear is an | Which activation function for output layer?
The reason why different activations exist is because they match different problems. Among a few others are mentioned by you "linear functions, sigmoid functions and softmax functions":
linear is an obvious choice for regression problems where you are predicting unbounded qu... | Which activation function for output layer?
The reason why different activations exist is because they match different problems. Among a few others are mentioned by you "linear functions, sigmoid functions and softmax functions":
linear is an |
3,005 | Which activation function for output layer? | As i see that lot of people have given very intuitive, and perfect answers for the question, but i will give my answer in a nutshell.
So,
For hidden layers the best option to use is ReLU, and the second option you can use as SIGMOID.
For output layers the best option depends, so we use LINEAR FUNCTIONS for regression ... | Which activation function for output layer? | As i see that lot of people have given very intuitive, and perfect answers for the question, but i will give my answer in a nutshell.
So,
For hidden layers the best option to use is ReLU, and the sec | Which activation function for output layer?
As i see that lot of people have given very intuitive, and perfect answers for the question, but i will give my answer in a nutshell.
So,
For hidden layers the best option to use is ReLU, and the second option you can use as SIGMOID.
For output layers the best option depends... | Which activation function for output layer?
As i see that lot of people have given very intuitive, and perfect answers for the question, but i will give my answer in a nutshell.
So,
For hidden layers the best option to use is ReLU, and the sec |
3,006 | Why is ANOVA equivalent to linear regression? | ANOVA and linear regression are equivalent when the two models test against the same hypotheses and use an identical encoding. The models differ in their basic aim: ANOVA is mostly concerned to present differences between categories' means in the data while linear regression is mostly concern to estimate a sample mean ... | Why is ANOVA equivalent to linear regression? | ANOVA and linear regression are equivalent when the two models test against the same hypotheses and use an identical encoding. The models differ in their basic aim: ANOVA is mostly concerned to presen | Why is ANOVA equivalent to linear regression?
ANOVA and linear regression are equivalent when the two models test against the same hypotheses and use an identical encoding. The models differ in their basic aim: ANOVA is mostly concerned to present differences between categories' means in the data while linear regressio... | Why is ANOVA equivalent to linear regression?
ANOVA and linear regression are equivalent when the two models test against the same hypotheses and use an identical encoding. The models differ in their basic aim: ANOVA is mostly concerned to presen |
3,007 | Why is ANOVA equivalent to linear regression? | Let me put some color into the idea that OLS with categorical (dummy-coded) regressors is equivalent to the factors in ANOVA. In both cases there are levels (or groups in the case of ANOVA).
In OLS regression it is most usual to have also continuous variables in the regressors. These logically modify the relationship ... | Why is ANOVA equivalent to linear regression? | Let me put some color into the idea that OLS with categorical (dummy-coded) regressors is equivalent to the factors in ANOVA. In both cases there are levels (or groups in the case of ANOVA).
In OLS r | Why is ANOVA equivalent to linear regression?
Let me put some color into the idea that OLS with categorical (dummy-coded) regressors is equivalent to the factors in ANOVA. In both cases there are levels (or groups in the case of ANOVA).
In OLS regression it is most usual to have also continuous variables in the regres... | Why is ANOVA equivalent to linear regression?
Let me put some color into the idea that OLS with categorical (dummy-coded) regressors is equivalent to the factors in ANOVA. In both cases there are levels (or groups in the case of ANOVA).
In OLS r |
3,008 | Why is ANOVA equivalent to linear regression? | Antoni Parellada and usεr11852 had very good answer. I will address your question for coding perspective with R.
ANOVA tells you nothing about the coefficients of the linear model. So how is linear regression the same as ANOVA?
In fact, we can aov function in R can be used as same as lm. Here are some examples.
> lm_... | Why is ANOVA equivalent to linear regression? | Antoni Parellada and usεr11852 had very good answer. I will address your question for coding perspective with R.
ANOVA tells you nothing about the coefficients of the linear model. So how is linear r | Why is ANOVA equivalent to linear regression?
Antoni Parellada and usεr11852 had very good answer. I will address your question for coding perspective with R.
ANOVA tells you nothing about the coefficients of the linear model. So how is linear regression the same as ANOVA?
In fact, we can aov function in R can be use... | Why is ANOVA equivalent to linear regression?
Antoni Parellada and usεr11852 had very good answer. I will address your question for coding perspective with R.
ANOVA tells you nothing about the coefficients of the linear model. So how is linear r |
3,009 | Why is ANOVA equivalent to linear regression? | ANOVA is not a model; it is a method within a model
The analysis of variance (ANOVA) is a method that occurs within regression models. The technique gives a particular set of outputs for the model that analyse the estimated variance of different parts, and use this to make inferences about whether or not there are rel... | Why is ANOVA equivalent to linear regression? | ANOVA is not a model; it is a method within a model
The analysis of variance (ANOVA) is a method that occurs within regression models. The technique gives a particular set of outputs for the model th | Why is ANOVA equivalent to linear regression?
ANOVA is not a model; it is a method within a model
The analysis of variance (ANOVA) is a method that occurs within regression models. The technique gives a particular set of outputs for the model that analyse the estimated variance of different parts, and use this to make... | Why is ANOVA equivalent to linear regression?
ANOVA is not a model; it is a method within a model
The analysis of variance (ANOVA) is a method that occurs within regression models. The technique gives a particular set of outputs for the model th |
3,010 | Why is ANOVA equivalent to linear regression? | If we take all data entries and arrange them into one single column Y, with the rest of the columns being indicator variables 1{ith data is element of the jth column in the original anova arrangement} then by taking a simple linear regression of Y on any of the other columns (say column B), you should obtain the same D... | Why is ANOVA equivalent to linear regression? | If we take all data entries and arrange them into one single column Y, with the rest of the columns being indicator variables 1{ith data is element of the jth column in the original anova arrangement} | Why is ANOVA equivalent to linear regression?
If we take all data entries and arrange them into one single column Y, with the rest of the columns being indicator variables 1{ith data is element of the jth column in the original anova arrangement} then by taking a simple linear regression of Y on any of the other column... | Why is ANOVA equivalent to linear regression?
If we take all data entries and arrange them into one single column Y, with the rest of the columns being indicator variables 1{ith data is element of the jth column in the original anova arrangement} |
3,011 | Standard error for the mean of a sample of binomial random variables | It seems like you're using $n$ twice in two different ways - both as the sample size and as the number of bernoulli trials that comprise the Binomial random variable; to eliminate any ambiguity, I'm going to use $k$ to refer to the latter.
If you have $n$ independent samples from a ${\rm Binomial}(k,p)$ distribution, ... | Standard error for the mean of a sample of binomial random variables | It seems like you're using $n$ twice in two different ways - both as the sample size and as the number of bernoulli trials that comprise the Binomial random variable; to eliminate any ambiguity, I'm g | Standard error for the mean of a sample of binomial random variables
It seems like you're using $n$ twice in two different ways - both as the sample size and as the number of bernoulli trials that comprise the Binomial random variable; to eliminate any ambiguity, I'm going to use $k$ to refer to the latter.
If you hav... | Standard error for the mean of a sample of binomial random variables
It seems like you're using $n$ twice in two different ways - both as the sample size and as the number of bernoulli trials that comprise the Binomial random variable; to eliminate any ambiguity, I'm g |
3,012 | Standard error for the mean of a sample of binomial random variables | It's easy to get two binomial distributions confused:
distribution of number of successes
distribution of the proportion of successes
npq is the number of successes, while npq/n = pq is the ratio of successes. This results in different standard error formulas. | Standard error for the mean of a sample of binomial random variables | It's easy to get two binomial distributions confused:
distribution of number of successes
distribution of the proportion of successes
npq is the number of successes, while npq/n = pq is the ratio of | Standard error for the mean of a sample of binomial random variables
It's easy to get two binomial distributions confused:
distribution of number of successes
distribution of the proportion of successes
npq is the number of successes, while npq/n = pq is the ratio of successes. This results in different standard erro... | Standard error for the mean of a sample of binomial random variables
It's easy to get two binomial distributions confused:
distribution of number of successes
distribution of the proportion of successes
npq is the number of successes, while npq/n = pq is the ratio of |
3,013 | Standard error for the mean of a sample of binomial random variables | We can look at this in the following way:
Suppose we are doing an experiment where we need to toss an unbiased coin $n$ times. The overall outcome of the experiment is $Y$ which is the summation of individual tosses (say, head as 1 and tail as 0). So, for this experiment, $Y = \sum_{i=1}^n X_i$, where $X_i$ are outcome... | Standard error for the mean of a sample of binomial random variables | We can look at this in the following way:
Suppose we are doing an experiment where we need to toss an unbiased coin $n$ times. The overall outcome of the experiment is $Y$ which is the summation of in | Standard error for the mean of a sample of binomial random variables
We can look at this in the following way:
Suppose we are doing an experiment where we need to toss an unbiased coin $n$ times. The overall outcome of the experiment is $Y$ which is the summation of individual tosses (say, head as 1 and tail as 0). So,... | Standard error for the mean of a sample of binomial random variables
We can look at this in the following way:
Suppose we are doing an experiment where we need to toss an unbiased coin $n$ times. The overall outcome of the experiment is $Y$ which is the summation of in |
3,014 | Standard error for the mean of a sample of binomial random variables | I think there is also some confusion in the initial post between standard error and standard deviation. Standard deviation is the sqrt of the variance of a distribution; standard error is the standard deviation of the estimated mean of a sample from that distribution, i.e., the spread of the means you would observe if ... | Standard error for the mean of a sample of binomial random variables | I think there is also some confusion in the initial post between standard error and standard deviation. Standard deviation is the sqrt of the variance of a distribution; standard error is the standard | Standard error for the mean of a sample of binomial random variables
I think there is also some confusion in the initial post between standard error and standard deviation. Standard deviation is the sqrt of the variance of a distribution; standard error is the standard deviation of the estimated mean of a sample from t... | Standard error for the mean of a sample of binomial random variables
I think there is also some confusion in the initial post between standard error and standard deviation. Standard deviation is the sqrt of the variance of a distribution; standard error is the standard |
3,015 | Do all interactions terms need their individual terms in regression model? | Most of the time this is a bad idea - the main reason is that it no longer makes the model invariant to location shifts. For example, suppose you have a single outcome $y_i$ and two predictors $x_i$ and $z_i$ and specify the model:
$$ y_i = \beta_0 + \beta_1 x_{i} z_i + \varepsilon $$
If you were to center the predi... | Do all interactions terms need their individual terms in regression model? | Most of the time this is a bad idea - the main reason is that it no longer makes the model invariant to location shifts. For example, suppose you have a single outcome $y_i$ and two predictors $x_i$ a | Do all interactions terms need their individual terms in regression model?
Most of the time this is a bad idea - the main reason is that it no longer makes the model invariant to location shifts. For example, suppose you have a single outcome $y_i$ and two predictors $x_i$ and $z_i$ and specify the model:
$$ y_i = \b... | Do all interactions terms need their individual terms in regression model?
Most of the time this is a bad idea - the main reason is that it no longer makes the model invariant to location shifts. For example, suppose you have a single outcome $y_i$ and two predictors $x_i$ a |
3,016 | Do all interactions terms need their individual terms in regression model? | All the answers so far seem to miss a very basic point: the functional form you choose should be flexible enough to capture the features that are scientifically relevant. Models 2-5 impose zero coefficients on some terms without scientific justification. And even if scientifically justified, Model 1 remains appealing... | Do all interactions terms need their individual terms in regression model? | All the answers so far seem to miss a very basic point: the functional form you choose should be flexible enough to capture the features that are scientifically relevant. Models 2-5 impose zero coeff | Do all interactions terms need their individual terms in regression model?
All the answers so far seem to miss a very basic point: the functional form you choose should be flexible enough to capture the features that are scientifically relevant. Models 2-5 impose zero coefficients on some terms without scientific just... | Do all interactions terms need their individual terms in regression model?
All the answers so far seem to miss a very basic point: the functional form you choose should be flexible enough to capture the features that are scientifically relevant. Models 2-5 impose zero coeff |
3,017 | Do all interactions terms need their individual terms in regression model? | +1 to @Macro. Let me bring out what I think is a similar point that concerns when you have categorical predictors. A lot can depend on how they are coded. For example, reference cell (aka, 'dummy') coding uses 0 & 1, whereas effect coding uses -1, 0 & 1. Consider a simple case with two factors with two levels each, ... | Do all interactions terms need their individual terms in regression model? | +1 to @Macro. Let me bring out what I think is a similar point that concerns when you have categorical predictors. A lot can depend on how they are coded. For example, reference cell (aka, 'dummy') | Do all interactions terms need their individual terms in regression model?
+1 to @Macro. Let me bring out what I think is a similar point that concerns when you have categorical predictors. A lot can depend on how they are coded. For example, reference cell (aka, 'dummy') coding uses 0 & 1, whereas effect coding use... | Do all interactions terms need their individual terms in regression model?
+1 to @Macro. Let me bring out what I think is a similar point that concerns when you have categorical predictors. A lot can depend on how they are coded. For example, reference cell (aka, 'dummy') |
3,018 | Do all interactions terms need their individual terms in regression model? | Since you are reviewing a paper you might suggest that the authors discuss the issue of model hierarchy and justify their departure from it.
Here are some references:
Nelder JA. The selection of terms in response-surface models—how strong is the weak-heredity principle? The American Statistician. 1998;52:315–8. htt... | Do all interactions terms need their individual terms in regression model? | Since you are reviewing a paper you might suggest that the authors discuss the issue of model hierarchy and justify their departure from it.
Here are some references:
Nelder JA. The selection of te | Do all interactions terms need their individual terms in regression model?
Since you are reviewing a paper you might suggest that the authors discuss the issue of model hierarchy and justify their departure from it.
Here are some references:
Nelder JA. The selection of terms in response-surface models—how strong is ... | Do all interactions terms need their individual terms in regression model?
Since you are reviewing a paper you might suggest that the authors discuss the issue of model hierarchy and justify their departure from it.
Here are some references:
Nelder JA. The selection of te |
3,019 | Do all interactions terms need their individual terms in regression model? | I have had a real case that illustrates this. In the data, one of the variables represented group with 0-control and 1-treatment. The other predictor represented time period with 0-before treatment and 1-after treatment. The interaction was the main parameter of interest measuring the effect of the treatment, the di... | Do all interactions terms need their individual terms in regression model? | I have had a real case that illustrates this. In the data, one of the variables represented group with 0-control and 1-treatment. The other predictor represented time period with 0-before treatment | Do all interactions terms need their individual terms in regression model?
I have had a real case that illustrates this. In the data, one of the variables represented group with 0-control and 1-treatment. The other predictor represented time period with 0-before treatment and 1-after treatment. The interaction was t... | Do all interactions terms need their individual terms in regression model?
I have had a real case that illustrates this. In the data, one of the variables represented group with 0-control and 1-treatment. The other predictor represented time period with 0-before treatment |
3,020 | Do all interactions terms need their individual terms in regression model? | I agree with Peter. I think the rule is folklore. Why could we conceive of a situation where two variables would affect the model only because of an interaction. An analogy in chemistry is that two chemicals are totally inert by themselves but cause an explosion when mixed together. Mathematical/statistical niceties... | Do all interactions terms need their individual terms in regression model? | I agree with Peter. I think the rule is folklore. Why could we conceive of a situation where two variables would affect the model only because of an interaction. An analogy in chemistry is that two | Do all interactions terms need their individual terms in regression model?
I agree with Peter. I think the rule is folklore. Why could we conceive of a situation where two variables would affect the model only because of an interaction. An analogy in chemistry is that two chemicals are totally inert by themselves but... | Do all interactions terms need their individual terms in regression model?
I agree with Peter. I think the rule is folklore. Why could we conceive of a situation where two variables would affect the model only because of an interaction. An analogy in chemistry is that two |
3,021 | Do all interactions terms need their individual terms in regression model? | [trying to answer a part of the original question which seems left uncovered in most answers: "should AIC, as a model selection criterion be trusted?"]
AIC should be used more as a guideline, than a rule that should be taken as gospel.
The effectiveness of AIC (or BIC or any similar 'simple' criterion for model selecti... | Do all interactions terms need their individual terms in regression model? | [trying to answer a part of the original question which seems left uncovered in most answers: "should AIC, as a model selection criterion be trusted?"]
AIC should be used more as a guideline, than a r | Do all interactions terms need their individual terms in regression model?
[trying to answer a part of the original question which seems left uncovered in most answers: "should AIC, as a model selection criterion be trusted?"]
AIC should be used more as a guideline, than a rule that should be taken as gospel.
The effec... | Do all interactions terms need their individual terms in regression model?
[trying to answer a part of the original question which seems left uncovered in most answers: "should AIC, as a model selection criterion be trusted?"]
AIC should be used more as a guideline, than a r |
3,022 | How to actually plot a sample tree from randomForest::getTree()? [closed] | First (and easiest) solution: If you are not keen to stick with classical RF, as implemented in Andy Liaw's randomForest, you can try the party package which provides a different implementation of the original RF algorithm (use of conditional trees and aggregation scheme based on units weight average). Then, as reporte... | How to actually plot a sample tree from randomForest::getTree()? [closed] | First (and easiest) solution: If you are not keen to stick with classical RF, as implemented in Andy Liaw's randomForest, you can try the party package which provides a different implementation of the | How to actually plot a sample tree from randomForest::getTree()? [closed]
First (and easiest) solution: If you are not keen to stick with classical RF, as implemented in Andy Liaw's randomForest, you can try the party package which provides a different implementation of the original RF algorithm (use of conditional tre... | How to actually plot a sample tree from randomForest::getTree()? [closed]
First (and easiest) solution: If you are not keen to stick with classical RF, as implemented in Andy Liaw's randomForest, you can try the party package which provides a different implementation of the |
3,023 | How to actually plot a sample tree from randomForest::getTree()? [closed] | I'm four years late, but if you really want to stick to the randomForest package (and there are some good reasons to do so), and want to actually visualize the tree, you can use the reprtree package.
The package is not super well documented (you can find the docs here), but everything is pretty straightforward. To inst... | How to actually plot a sample tree from randomForest::getTree()? [closed] | I'm four years late, but if you really want to stick to the randomForest package (and there are some good reasons to do so), and want to actually visualize the tree, you can use the reprtree package.
| How to actually plot a sample tree from randomForest::getTree()? [closed]
I'm four years late, but if you really want to stick to the randomForest package (and there are some good reasons to do so), and want to actually visualize the tree, you can use the reprtree package.
The package is not super well documented (you ... | How to actually plot a sample tree from randomForest::getTree()? [closed]
I'm four years late, but if you really want to stick to the randomForest package (and there are some good reasons to do so), and want to actually visualize the tree, you can use the reprtree package.
|
3,024 | How to actually plot a sample tree from randomForest::getTree()? [closed] | I've created some functions to extract the rules of a tree.
#***********************
#return the rules of a tree
#***********************
getConds<-function(tree){
#store all conditions into a list
conds<-list()
#start by the terminal nodes and find previous conditions
id.leafs<-which(tree$status==-1)
j<-0
... | How to actually plot a sample tree from randomForest::getTree()? [closed] | I've created some functions to extract the rules of a tree.
#***********************
#return the rules of a tree
#***********************
getConds<-function(tree){
#store all conditions into a list
| How to actually plot a sample tree from randomForest::getTree()? [closed]
I've created some functions to extract the rules of a tree.
#***********************
#return the rules of a tree
#***********************
getConds<-function(tree){
#store all conditions into a list
conds<-list()
#start by the terminal nodes... | How to actually plot a sample tree from randomForest::getTree()? [closed]
I've created some functions to extract the rules of a tree.
#***********************
#return the rules of a tree
#***********************
getConds<-function(tree){
#store all conditions into a list
|
3,025 | What's the difference between momentum based gradient descent and Nesterov's accelerated gradient descent? | Arech's answer about Nesterov momentum is correct, but the code essentially does the same thing. So in this regard the Nesterov method does give more weight to the $lr \cdot g$ term, and less weight to the $v$ term.
To illustrate why Keras' implementation is correct, I'll borrow Geoffrey Hinton's example.
Nesterov me... | What's the difference between momentum based gradient descent and Nesterov's accelerated gradient de | Arech's answer about Nesterov momentum is correct, but the code essentially does the same thing. So in this regard the Nesterov method does give more weight to the $lr \cdot g$ term, and less weight | What's the difference between momentum based gradient descent and Nesterov's accelerated gradient descent?
Arech's answer about Nesterov momentum is correct, but the code essentially does the same thing. So in this regard the Nesterov method does give more weight to the $lr \cdot g$ term, and less weight to the $v$ te... | What's the difference between momentum based gradient descent and Nesterov's accelerated gradient de
Arech's answer about Nesterov momentum is correct, but the code essentially does the same thing. So in this regard the Nesterov method does give more weight to the $lr \cdot g$ term, and less weight |
3,026 | What's the difference between momentum based gradient descent and Nesterov's accelerated gradient descent? | It seems to me that the OP's question was already answered, but I would try to give another (hopefully intuitive) explanation about momentum and the difference between Classical Momentum (CM) and Nesterov's Accelerated Gradient (NAG).
tl;dr
Just skip to the image at the end.
NAG_ball's reasoning is another important p... | What's the difference between momentum based gradient descent and Nesterov's accelerated gradient de | It seems to me that the OP's question was already answered, but I would try to give another (hopefully intuitive) explanation about momentum and the difference between Classical Momentum (CM) and Nest | What's the difference between momentum based gradient descent and Nesterov's accelerated gradient descent?
It seems to me that the OP's question was already answered, but I would try to give another (hopefully intuitive) explanation about momentum and the difference between Classical Momentum (CM) and Nesterov's Accele... | What's the difference between momentum based gradient descent and Nesterov's accelerated gradient de
It seems to me that the OP's question was already answered, but I would try to give another (hopefully intuitive) explanation about momentum and the difference between Classical Momentum (CM) and Nest |
3,027 | What's the difference between momentum based gradient descent and Nesterov's accelerated gradient descent? | I don't think so.
There's a good description of Nesterov Momentum (aka Nesterov Accelerated Gradient) properties in, for example, Sutskever, Martens et al."On the importance of initialization and momentum in deep learning" 2013.
The main difference is in classical momentum you first correct your velocity and then make ... | What's the difference between momentum based gradient descent and Nesterov's accelerated gradient de | I don't think so.
There's a good description of Nesterov Momentum (aka Nesterov Accelerated Gradient) properties in, for example, Sutskever, Martens et al."On the importance of initialization and mome | What's the difference between momentum based gradient descent and Nesterov's accelerated gradient descent?
I don't think so.
There's a good description of Nesterov Momentum (aka Nesterov Accelerated Gradient) properties in, for example, Sutskever, Martens et al."On the importance of initialization and momentum in deep ... | What's the difference between momentum based gradient descent and Nesterov's accelerated gradient de
I don't think so.
There's a good description of Nesterov Momentum (aka Nesterov Accelerated Gradient) properties in, for example, Sutskever, Martens et al."On the importance of initialization and mome |
3,028 | What's the difference between momentum based gradient descent and Nesterov's accelerated gradient descent? | Added: a Stanford course on neural networks,
cs231n,
gives yet another form of the steps:
v = mu * v_prev - learning_rate * gradient(x) # GD + momentum
v_nesterov = v + mu * (v - v_prev) # keep going, extrapolate
x += v_nesterov
Here v is velocity aka step aka state,
and mu is a momentum factor, typical... | What's the difference between momentum based gradient descent and Nesterov's accelerated gradient de | Added: a Stanford course on neural networks,
cs231n,
gives yet another form of the steps:
v = mu * v_prev - learning_rate * gradient(x) # GD + momentum
v_nesterov = v + mu * (v - v_prev) | What's the difference between momentum based gradient descent and Nesterov's accelerated gradient descent?
Added: a Stanford course on neural networks,
cs231n,
gives yet another form of the steps:
v = mu * v_prev - learning_rate * gradient(x) # GD + momentum
v_nesterov = v + mu * (v - v_prev) # keep goin... | What's the difference between momentum based gradient descent and Nesterov's accelerated gradient de
Added: a Stanford course on neural networks,
cs231n,
gives yet another form of the steps:
v = mu * v_prev - learning_rate * gradient(x) # GD + momentum
v_nesterov = v + mu * (v - v_prev) |
3,029 | What's the difference between momentum based gradient descent and Nesterov's accelerated gradient descent? | You say :
So to me it seems Nesterov's accelerated gradient descent just gives more weight to the ηg term over the pervious weight change term m (compared to plain old momentum).
The answer is : NO.
As you noticed : $p_{new} = p + \beta^2 m - (1+\beta)\eta g$. You might think it is equivalent to an update like this :... | What's the difference between momentum based gradient descent and Nesterov's accelerated gradient de | You say :
So to me it seems Nesterov's accelerated gradient descent just gives more weight to the ηg term over the pervious weight change term m (compared to plain old momentum).
The answer is : NO. | What's the difference between momentum based gradient descent and Nesterov's accelerated gradient descent?
You say :
So to me it seems Nesterov's accelerated gradient descent just gives more weight to the ηg term over the pervious weight change term m (compared to plain old momentum).
The answer is : NO.
As you notic... | What's the difference between momentum based gradient descent and Nesterov's accelerated gradient de
You say :
So to me it seems Nesterov's accelerated gradient descent just gives more weight to the ηg term over the pervious weight change term m (compared to plain old momentum).
The answer is : NO. |
3,030 | What is the difference between discrete data and continuous data? | Discrete data can only take particular values. There may potentially be an infinite number of those values, but each is distinct and there's no grey area in between. Discrete data can be numeric -- like numbers of apples -- but it can also be categorical -- like red or blue, or male or female, or good or bad.
Continuou... | What is the difference between discrete data and continuous data? | Discrete data can only take particular values. There may potentially be an infinite number of those values, but each is distinct and there's no grey area in between. Discrete data can be numeric -- li | What is the difference between discrete data and continuous data?
Discrete data can only take particular values. There may potentially be an infinite number of those values, but each is distinct and there's no grey area in between. Discrete data can be numeric -- like numbers of apples -- but it can also be categorical... | What is the difference between discrete data and continuous data?
Discrete data can only take particular values. There may potentially be an infinite number of those values, but each is distinct and there's no grey area in between. Discrete data can be numeric -- li |
3,031 | What is the difference between discrete data and continuous data? | Data is always discrete. Given a sample of n values on a variable, the maximum number of distinct values the variable can take is equal to n. See this quote
All actual sample spaces are discrete, and all observable random
variables have discrete distributions. The continuous distribution is
a mathematical constr... | What is the difference between discrete data and continuous data? | Data is always discrete. Given a sample of n values on a variable, the maximum number of distinct values the variable can take is equal to n. See this quote
All actual sample spaces are discrete, a | What is the difference between discrete data and continuous data?
Data is always discrete. Given a sample of n values on a variable, the maximum number of distinct values the variable can take is equal to n. See this quote
All actual sample spaces are discrete, and all observable random
variables have discrete dis... | What is the difference between discrete data and continuous data?
Data is always discrete. Given a sample of n values on a variable, the maximum number of distinct values the variable can take is equal to n. See this quote
All actual sample spaces are discrete, a |
3,032 | What is the difference between discrete data and continuous data? | Temperatures are continuous. It can be 23 degrees, 23.1 degrees, 23.100004 degrees.
Sex is discrete. You can only be male or female (in classical thinking anyways). Something you could represent with a whole number like 1, 2, etc
The difference is important as many statistical and data mining algorithms can handle one... | What is the difference between discrete data and continuous data? | Temperatures are continuous. It can be 23 degrees, 23.1 degrees, 23.100004 degrees.
Sex is discrete. You can only be male or female (in classical thinking anyways). Something you could represent with | What is the difference between discrete data and continuous data?
Temperatures are continuous. It can be 23 degrees, 23.1 degrees, 23.100004 degrees.
Sex is discrete. You can only be male or female (in classical thinking anyways). Something you could represent with a whole number like 1, 2, etc
The difference is impor... | What is the difference between discrete data and continuous data?
Temperatures are continuous. It can be 23 degrees, 23.1 degrees, 23.100004 degrees.
Sex is discrete. You can only be male or female (in classical thinking anyways). Something you could represent with |
3,033 | What is the difference between discrete data and continuous data? | Discrete Data can only take certain values.
Example: the number of students in a class (you can't have half a student).
Continuous Data is data that can take any value (within a range)
Examples:
A person's height: could be any value (within the range of human heights), not just certain fixed heights,
Time in a race:... | What is the difference between discrete data and continuous data? | Discrete Data can only take certain values.
Example: the number of students in a class (you can't have half a student).
Continuous Data is data that can take any value (within a range)
Examples:
A pe | What is the difference between discrete data and continuous data?
Discrete Data can only take certain values.
Example: the number of students in a class (you can't have half a student).
Continuous Data is data that can take any value (within a range)
Examples:
A person's height: could be any value (within the range of... | What is the difference between discrete data and continuous data?
Discrete Data can only take certain values.
Example: the number of students in a class (you can't have half a student).
Continuous Data is data that can take any value (within a range)
Examples:
A pe |
3,034 | What is the difference between discrete data and continuous data? | In the case of database, we would always store the data in discrete even the nature of the data is continuous. Why should I emphasize the nature of data? We should take the distribution of data that could help us to analyze the data. IF the nature of data is continuous, I suggest you to use them by continuous analysis.... | What is the difference between discrete data and continuous data? | In the case of database, we would always store the data in discrete even the nature of the data is continuous. Why should I emphasize the nature of data? We should take the distribution of data that c | What is the difference between discrete data and continuous data?
In the case of database, we would always store the data in discrete even the nature of the data is continuous. Why should I emphasize the nature of data? We should take the distribution of data that could help us to analyze the data. IF the nature of dat... | What is the difference between discrete data and continuous data?
In the case of database, we would always store the data in discrete even the nature of the data is continuous. Why should I emphasize the nature of data? We should take the distribution of data that c |
3,035 | What is the difference between discrete data and continuous data? | On the one hand, from a practical point of view I do agree with Jeromy Anglim's answer. In the end we are most of the time dealing with discrete variables – although from a theoretical point of view they are continuous – and that has a real impact for instance for classification. Recall Strobl's paper indicating that R... | What is the difference between discrete data and continuous data? | On the one hand, from a practical point of view I do agree with Jeromy Anglim's answer. In the end we are most of the time dealing with discrete variables – although from a theoretical point of view t | What is the difference between discrete data and continuous data?
On the one hand, from a practical point of view I do agree with Jeromy Anglim's answer. In the end we are most of the time dealing with discrete variables – although from a theoretical point of view they are continuous – and that has a real impact for in... | What is the difference between discrete data and continuous data?
On the one hand, from a practical point of view I do agree with Jeromy Anglim's answer. In the end we are most of the time dealing with discrete variables – although from a theoretical point of view t |
3,036 | What is the difference between discrete data and continuous data? | Discrete data take particular values, while continuous data are not restricted to separate values.
Discrete data are distinct and there is no grey area in between, while continuous data occupy any value over a continuous data value. | What is the difference between discrete data and continuous data? | Discrete data take particular values, while continuous data are not restricted to separate values.
Discrete data are distinct and there is no grey area in between, while continuous data occupy any va | What is the difference between discrete data and continuous data?
Discrete data take particular values, while continuous data are not restricted to separate values.
Discrete data are distinct and there is no grey area in between, while continuous data occupy any value over a continuous data value. | What is the difference between discrete data and continuous data?
Discrete data take particular values, while continuous data are not restricted to separate values.
Discrete data are distinct and there is no grey area in between, while continuous data occupy any va |
3,037 | What is the difference between discrete data and continuous data? | Discrete data
They can take particular values .they are numeric. | What is the difference between discrete data and continuous data? | Discrete data
They can take particular values .they are numeric. | What is the difference between discrete data and continuous data?
Discrete data
They can take particular values .they are numeric. | What is the difference between discrete data and continuous data?
Discrete data
They can take particular values .they are numeric. |
3,038 | What is the difference between discrete data and continuous data? | Discrete data can take on only integer values whereas continuous data can take on any value. For instance the number of cancer patients treated by a hospital each year is discrete but your weight is continuous. Some data are continuous but measured in a discrete way e.g. your age. It is common to report your age as say... | What is the difference between discrete data and continuous data? | Discrete data can take on only integer values whereas continuous data can take on any value. For instance the number of cancer patients treated by a hospital each year is discrete but your weight is c | What is the difference between discrete data and continuous data?
Discrete data can take on only integer values whereas continuous data can take on any value. For instance the number of cancer patients treated by a hospital each year is discrete but your weight is continuous. Some data are continuous but measured in a ... | What is the difference between discrete data and continuous data?
Discrete data can take on only integer values whereas continuous data can take on any value. For instance the number of cancer patients treated by a hospital each year is discrete but your weight is c |
3,039 | What is the difference between discrete data and continuous data? | Discrete data perticularly talks about the finite values and continuous data talks about ifinite values..... | What is the difference between discrete data and continuous data? | Discrete data perticularly talks about the finite values and continuous data talks about ifinite values..... | What is the difference between discrete data and continuous data?
Discrete data perticularly talks about the finite values and continuous data talks about ifinite values..... | What is the difference between discrete data and continuous data?
Discrete data perticularly talks about the finite values and continuous data talks about ifinite values..... |
3,040 | Complete substantive examples of reproducible research using R | Frank Harrell has been beating the drum on reproducible research and reports for many, many years. You could start
at this wiki page which lists plenty of other resources, including published research and also covers Charles Geyer's page. | Complete substantive examples of reproducible research using R | Frank Harrell has been beating the drum on reproducible research and reports for many, many years. You could start
at this wiki page which lists plenty of other resources, including published resear | Complete substantive examples of reproducible research using R
Frank Harrell has been beating the drum on reproducible research and reports for many, many years. You could start
at this wiki page which lists plenty of other resources, including published research and also covers Charles Geyer's page. | Complete substantive examples of reproducible research using R
Frank Harrell has been beating the drum on reproducible research and reports for many, many years. You could start
at this wiki page which lists plenty of other resources, including published resear |
3,041 | Complete substantive examples of reproducible research using R | The journal Biostatistics has an Associate Editor for Reproducibility, and all its articles are marked:
Reproducible Research
Our reproducible research policy is for papers in the journal to be
kite-marked D if the data on which they are based are freely
available, C if the authors’ code is freely available, and R... | Complete substantive examples of reproducible research using R | The journal Biostatistics has an Associate Editor for Reproducibility, and all its articles are marked:
Reproducible Research
Our reproducible research policy is for papers in the journal to be
kit | Complete substantive examples of reproducible research using R
The journal Biostatistics has an Associate Editor for Reproducibility, and all its articles are marked:
Reproducible Research
Our reproducible research policy is for papers in the journal to be
kite-marked D if the data on which they are based are freely... | Complete substantive examples of reproducible research using R
The journal Biostatistics has an Associate Editor for Reproducibility, and all its articles are marked:
Reproducible Research
Our reproducible research policy is for papers in the journal to be
kit |
3,042 | Complete substantive examples of reproducible research using R | Irreproducibility of NCI60 Predictors of Chemotherapy
This is a reproducible analysis showing the lack of reproducibility of a paper that has been in the news. A clinical trial based on the false conclusions of the irreproducible paper was suspended, re-instated, suspended again, ... It's a good example of reproducibl... | Complete substantive examples of reproducible research using R | Irreproducibility of NCI60 Predictors of Chemotherapy
This is a reproducible analysis showing the lack of reproducibility of a paper that has been in the news. A clinical trial based on the false con | Complete substantive examples of reproducible research using R
Irreproducibility of NCI60 Predictors of Chemotherapy
This is a reproducible analysis showing the lack of reproducibility of a paper that has been in the news. A clinical trial based on the false conclusions of the irreproducible paper was suspended, re-in... | Complete substantive examples of reproducible research using R
Irreproducibility of NCI60 Predictors of Chemotherapy
This is a reproducible analysis showing the lack of reproducibility of a paper that has been in the news. A clinical trial based on the false con |
3,043 | Complete substantive examples of reproducible research using R | I have a few such examples on my research papers page. (I am not allowed to post more than one hyperlink as a new member. So I'll just describe the papers on that site.)
(1) "Making Effects Manifest in Randomized Experiments" uses R's vignette system.
(2) "Attributing Effects to a Cluster Randomized Get-Out-The-Vote ... | Complete substantive examples of reproducible research using R | I have a few such examples on my research papers page. (I am not allowed to post more than one hyperlink as a new member. So I'll just describe the papers on that site.)
(1) "Making Effects Manifest i | Complete substantive examples of reproducible research using R
I have a few such examples on my research papers page. (I am not allowed to post more than one hyperlink as a new member. So I'll just describe the papers on that site.)
(1) "Making Effects Manifest in Randomized Experiments" uses R's vignette system.
(2)... | Complete substantive examples of reproducible research using R
I have a few such examples on my research papers page. (I am not allowed to post more than one hyperlink as a new member. So I'll just describe the papers on that site.)
(1) "Making Effects Manifest i |
3,044 | Complete substantive examples of reproducible research using R | Koenker and Zeileis provide a webpage with a relatively complete example.
They share:
Rnw (Sweave code)
R analysis code
Final PDF
Discussion of version control issues | Complete substantive examples of reproducible research using R | Koenker and Zeileis provide a webpage with a relatively complete example.
They share:
Rnw (Sweave code)
R analysis code
Final PDF
Discussion of version control issues | Complete substantive examples of reproducible research using R
Koenker and Zeileis provide a webpage with a relatively complete example.
They share:
Rnw (Sweave code)
R analysis code
Final PDF
Discussion of version control issues | Complete substantive examples of reproducible research using R
Koenker and Zeileis provide a webpage with a relatively complete example.
They share:
Rnw (Sweave code)
R analysis code
Final PDF
Discussion of version control issues |
3,045 | Complete substantive examples of reproducible research using R | We wrote a paper explaining how to use R/Bioconductor when analysing microarray data. The paper was written in Sweave and all the code used to generate the graphs is included as supplementary material.
Gillespie, C. S., Lei, G., Boys, R. J., Greenall, A. J., Wilkinson, D. J., 2010. Analysing yeast time course microarra... | Complete substantive examples of reproducible research using R | We wrote a paper explaining how to use R/Bioconductor when analysing microarray data. The paper was written in Sweave and all the code used to generate the graphs is included as supplementary material | Complete substantive examples of reproducible research using R
We wrote a paper explaining how to use R/Bioconductor when analysing microarray data. The paper was written in Sweave and all the code used to generate the graphs is included as supplementary material.
Gillespie, C. S., Lei, G., Boys, R. J., Greenall, A. J.... | Complete substantive examples of reproducible research using R
We wrote a paper explaining how to use R/Bioconductor when analysing microarray data. The paper was written in Sweave and all the code used to generate the graphs is included as supplementary material |
3,046 | Complete substantive examples of reproducible research using R | Charles Geyer's page on Sweave has an example from a thesis, which meets some of your requirements (the raw data is simply from an R package, but the R/sweave code and final PDF are available):
A paper on the theory in Yun Ju Sung's thesis, Monte Carlo Likelihood Inference for Missing Data Models (preprint) contained ... | Complete substantive examples of reproducible research using R | Charles Geyer's page on Sweave has an example from a thesis, which meets some of your requirements (the raw data is simply from an R package, but the R/sweave code and final PDF are available):
A pap | Complete substantive examples of reproducible research using R
Charles Geyer's page on Sweave has an example from a thesis, which meets some of your requirements (the raw data is simply from an R package, but the R/sweave code and final PDF are available):
A paper on the theory in Yun Ju Sung's thesis, Monte Carlo Lik... | Complete substantive examples of reproducible research using R
Charles Geyer's page on Sweave has an example from a thesis, which meets some of your requirements (the raw data is simply from an R package, but the R/sweave code and final PDF are available):
A pap |
3,047 | Complete substantive examples of reproducible research using R | Simon Jackman has a particularly useful example of analysing the results of a survey: "Americans and Australians 10 years after 9/11". It has multiple examples of integrating tables and figures.
He has made the Sweave document and PDF report via this blog post.
While the raw data is not supplied (as far as I can tell... | Complete substantive examples of reproducible research using R | Simon Jackman has a particularly useful example of analysing the results of a survey: "Americans and Australians 10 years after 9/11". It has multiple examples of integrating tables and figures.
He h | Complete substantive examples of reproducible research using R
Simon Jackman has a particularly useful example of analysing the results of a survey: "Americans and Australians 10 years after 9/11". It has multiple examples of integrating tables and figures.
He has made the Sweave document and PDF report via this blog... | Complete substantive examples of reproducible research using R
Simon Jackman has a particularly useful example of analysing the results of a survey: "Americans and Australians 10 years after 9/11". It has multiple examples of integrating tables and figures.
He h |
3,048 | Complete substantive examples of reproducible research using R | Neil Saunders analysed online interactions associated with a conference.
Several properties which make it a useful Sweave example include:
Rnw file is provided
Graphs are generated using ggplot
Good size and easily comprehensible domain
The materials are available here:
The blog post
The github repository
Rnw file o... | Complete substantive examples of reproducible research using R | Neil Saunders analysed online interactions associated with a conference.
Several properties which make it a useful Sweave example include:
Rnw file is provided
Graphs are generated using ggplot
Good | Complete substantive examples of reproducible research using R
Neil Saunders analysed online interactions associated with a conference.
Several properties which make it a useful Sweave example include:
Rnw file is provided
Graphs are generated using ggplot
Good size and easily comprehensible domain
The materials are ... | Complete substantive examples of reproducible research using R
Neil Saunders analysed online interactions associated with a conference.
Several properties which make it a useful Sweave example include:
Rnw file is provided
Graphs are generated using ggplot
Good |
3,049 | Complete substantive examples of reproducible research using R | Also look at Journal Of Statistical Software; they encourage making papers in Sweave. | Complete substantive examples of reproducible research using R | Also look at Journal Of Statistical Software; they encourage making papers in Sweave. | Complete substantive examples of reproducible research using R
Also look at Journal Of Statistical Software; they encourage making papers in Sweave. | Complete substantive examples of reproducible research using R
Also look at Journal Of Statistical Software; they encourage making papers in Sweave. |
3,050 | Complete substantive examples of reproducible research using R | I have found good ones in the past and will post once I dig them up, but some quick general suggestions:
You may be able to find some interesting examples by searching google with keywords and ext:rnw (which will search for files with the sweave extension). Here's an example search. This is the third result from my s... | Complete substantive examples of reproducible research using R | I have found good ones in the past and will post once I dig them up, but some quick general suggestions:
You may be able to find some interesting examples by searching google with keywords and ext:rn | Complete substantive examples of reproducible research using R
I have found good ones in the past and will post once I dig them up, but some quick general suggestions:
You may be able to find some interesting examples by searching google with keywords and ext:rnw (which will search for files with the sweave extension)... | Complete substantive examples of reproducible research using R
I have found good ones in the past and will post once I dig them up, but some quick general suggestions:
You may be able to find some interesting examples by searching google with keywords and ext:rn |
3,051 | Complete substantive examples of reproducible research using R | Robert Gentleman wrote a paper called "Reproducible Research: A Bioinformatics Case Study"
It implements a short set of analyses as an R Package and uses Sweave.
It also discusses the use of Sweave more generally.
See the "Related Files" section of the article page for an archive file of all files and folders used.
Ref... | Complete substantive examples of reproducible research using R | Robert Gentleman wrote a paper called "Reproducible Research: A Bioinformatics Case Study"
It implements a short set of analyses as an R Package and uses Sweave.
It also discusses the use of Sweave mo | Complete substantive examples of reproducible research using R
Robert Gentleman wrote a paper called "Reproducible Research: A Bioinformatics Case Study"
It implements a short set of analyses as an R Package and uses Sweave.
It also discusses the use of Sweave more generally.
See the "Related Files" section of the arti... | Complete substantive examples of reproducible research using R
Robert Gentleman wrote a paper called "Reproducible Research: A Bioinformatics Case Study"
It implements a short set of analyses as an R Package and uses Sweave.
It also discusses the use of Sweave mo |
3,052 | Complete substantive examples of reproducible research using R | http://genome.cshlp.org/content/early/2011/06/09/gr.117523.110/suppl/DC1
A nice paper, by a lab mate of mine. Our PI was pretty pleased when something resembling fan mail came in for this. Now all publications from the group have the supplemental methods laid out in LaTeX/Sweave. Some of the papers, too (can't decid... | Complete substantive examples of reproducible research using R | http://genome.cshlp.org/content/early/2011/06/09/gr.117523.110/suppl/DC1
A nice paper, by a lab mate of mine. Our PI was pretty pleased when something resembling fan mail came in for this. Now all p | Complete substantive examples of reproducible research using R
http://genome.cshlp.org/content/early/2011/06/09/gr.117523.110/suppl/DC1
A nice paper, by a lab mate of mine. Our PI was pretty pleased when something resembling fan mail came in for this. Now all publications from the group have the supplemental methods ... | Complete substantive examples of reproducible research using R
http://genome.cshlp.org/content/early/2011/06/09/gr.117523.110/suppl/DC1
A nice paper, by a lab mate of mine. Our PI was pretty pleased when something resembling fan mail came in for this. Now all p |
3,053 | Complete substantive examples of reproducible research using R | Looking for examples and practices is a good way to learn, but I just wanted to mention that reproducibility has not only technical/script rerun side, but also code style and structuring aspect, minimization of side effects in core functions etc.
I personally found that Chambers book Software for Data Analysis allows ... | Complete substantive examples of reproducible research using R | Looking for examples and practices is a good way to learn, but I just wanted to mention that reproducibility has not only technical/script rerun side, but also code style and structuring aspect, minim | Complete substantive examples of reproducible research using R
Looking for examples and practices is a good way to learn, but I just wanted to mention that reproducibility has not only technical/script rerun side, but also code style and structuring aspect, minimization of side effects in core functions etc.
I persona... | Complete substantive examples of reproducible research using R
Looking for examples and practices is a good way to learn, but I just wanted to mention that reproducibility has not only technical/script rerun side, but also code style and structuring aspect, minim |
3,054 | Complete substantive examples of reproducible research using R | if you still need a great example of a fully REPRODUCIBLE analysis plus a PAPER, use this repo.
The @jscamac did a great job by making his analysis rproducible and I personally validated it.
You can lean how to use R specific functions like the package remake to ensure reproduciblity.
Watch out / the calculations ta... | Complete substantive examples of reproducible research using R | if you still need a great example of a fully REPRODUCIBLE analysis plus a PAPER, use this repo.
The @jscamac did a great job by making his analysis rproducible and I personally validated it.
You can | Complete substantive examples of reproducible research using R
if you still need a great example of a fully REPRODUCIBLE analysis plus a PAPER, use this repo.
The @jscamac did a great job by making his analysis rproducible and I personally validated it.
You can lean how to use R specific functions like the package re... | Complete substantive examples of reproducible research using R
if you still need a great example of a fully REPRODUCIBLE analysis plus a PAPER, use this repo.
The @jscamac did a great job by making his analysis rproducible and I personally validated it.
You can |
3,055 | What are the differences between 'epoch', 'batch', and 'minibatch'? | Epoch means one pass over the full training set
Batch means that you use all your data to compute the gradient during one iteration.
Mini-batch means you only take a subset of all your data during one iteration. | What are the differences between 'epoch', 'batch', and 'minibatch'? | Epoch means one pass over the full training set
Batch means that you use all your data to compute the gradient during one iteration.
Mini-batch means you only take a subset of all your data during one | What are the differences between 'epoch', 'batch', and 'minibatch'?
Epoch means one pass over the full training set
Batch means that you use all your data to compute the gradient during one iteration.
Mini-batch means you only take a subset of all your data during one iteration. | What are the differences between 'epoch', 'batch', and 'minibatch'?
Epoch means one pass over the full training set
Batch means that you use all your data to compute the gradient during one iteration.
Mini-batch means you only take a subset of all your data during one |
3,056 | What are the differences between 'epoch', 'batch', and 'minibatch'? | One epoch typically means your algorithm sees every training instance once. Now assuming you have $n$ training instances:
If you run batch update, every parameter update requires your algorithm see each of the $n$ training instances exactly once, i.e., every epoch your parameters are updated once.
If you run mini-batc... | What are the differences between 'epoch', 'batch', and 'minibatch'? | One epoch typically means your algorithm sees every training instance once. Now assuming you have $n$ training instances:
If you run batch update, every parameter update requires your algorithm see e | What are the differences between 'epoch', 'batch', and 'minibatch'?
One epoch typically means your algorithm sees every training instance once. Now assuming you have $n$ training instances:
If you run batch update, every parameter update requires your algorithm see each of the $n$ training instances exactly once, i.e.... | What are the differences between 'epoch', 'batch', and 'minibatch'?
One epoch typically means your algorithm sees every training instance once. Now assuming you have $n$ training instances:
If you run batch update, every parameter update requires your algorithm see e |
3,057 | What are the differences between 'epoch', 'batch', and 'minibatch'? | An epoch is typically one loop over the entire dataset.
A batch or minibatch refers to equally sized subsets of the dataset over which the gradient is calculated and weights updated.
i.e. for a dataset of size $n$:
Optimization method
Samples in each gradient calculation
Weight updates per epoch
Batch Gradient ... | What are the differences between 'epoch', 'batch', and 'minibatch'? | An epoch is typically one loop over the entire dataset.
A batch or minibatch refers to equally sized subsets of the dataset over which the gradient is calculated and weights updated.
i.e. for a datase | What are the differences between 'epoch', 'batch', and 'minibatch'?
An epoch is typically one loop over the entire dataset.
A batch or minibatch refers to equally sized subsets of the dataset over which the gradient is calculated and weights updated.
i.e. for a dataset of size $n$:
Optimization method
Samples in ea... | What are the differences between 'epoch', 'batch', and 'minibatch'?
An epoch is typically one loop over the entire dataset.
A batch or minibatch refers to equally sized subsets of the dataset over which the gradient is calculated and weights updated.
i.e. for a datase |
3,058 | What are the differences between 'epoch', 'batch', and 'minibatch'? | "Epoch" is usually means exposing a learning algorithm to the entire set of training data. This doesn't always make sense as we sometimes generate data.
"Batch" and "Minibatch" can be confusing.
Training examples sometimes need to be "batched" because not all data can necessarily be exposed to the algorithm at once (du... | What are the differences between 'epoch', 'batch', and 'minibatch'? | "Epoch" is usually means exposing a learning algorithm to the entire set of training data. This doesn't always make sense as we sometimes generate data.
"Batch" and "Minibatch" can be confusing.
Train | What are the differences between 'epoch', 'batch', and 'minibatch'?
"Epoch" is usually means exposing a learning algorithm to the entire set of training data. This doesn't always make sense as we sometimes generate data.
"Batch" and "Minibatch" can be confusing.
Training examples sometimes need to be "batched" because ... | What are the differences between 'epoch', 'batch', and 'minibatch'?
"Epoch" is usually means exposing a learning algorithm to the entire set of training data. This doesn't always make sense as we sometimes generate data.
"Batch" and "Minibatch" can be confusing.
Train |
3,059 | How small a quantity should be added to x to avoid taking the log of zero? | As the zeros merely indicate concentrations below the detection limit, maybe setting them to (detection limit)/2 would be appropriate
I was just typing that the thing that comes to my mind where log does (frequently) make sense and 0 may occur are concentrations when you did the 2nd edit. As you say, for measured conc... | How small a quantity should be added to x to avoid taking the log of zero? | As the zeros merely indicate concentrations below the detection limit, maybe setting them to (detection limit)/2 would be appropriate
I was just typing that the thing that comes to my mind where log | How small a quantity should be added to x to avoid taking the log of zero?
As the zeros merely indicate concentrations below the detection limit, maybe setting them to (detection limit)/2 would be appropriate
I was just typing that the thing that comes to my mind where log does (frequently) make sense and 0 may occur ... | How small a quantity should be added to x to avoid taking the log of zero?
As the zeros merely indicate concentrations below the detection limit, maybe setting them to (detection limit)/2 would be appropriate
I was just typing that the thing that comes to my mind where log |
3,060 | How small a quantity should be added to x to avoid taking the log of zero? | Chemical concentration data often have zeros, but these do not represent zero values: they are codes that variously (and confusingly) represent both nondetects (the measurement indicated, with a high degree of likelihood, that the analyte was not present) and "unquantified" values (the measurement detected the analyte ... | How small a quantity should be added to x to avoid taking the log of zero? | Chemical concentration data often have zeros, but these do not represent zero values: they are codes that variously (and confusingly) represent both nondetects (the measurement indicated, with a high | How small a quantity should be added to x to avoid taking the log of zero?
Chemical concentration data often have zeros, but these do not represent zero values: they are codes that variously (and confusingly) represent both nondetects (the measurement indicated, with a high degree of likelihood, that the analyte was no... | How small a quantity should be added to x to avoid taking the log of zero?
Chemical concentration data often have zeros, but these do not represent zero values: they are codes that variously (and confusingly) represent both nondetects (the measurement indicated, with a high |
3,061 | How small a quantity should be added to x to avoid taking the log of zero? | @miura
I came across this article by Bill Gould on the Stata blog (I think he actually founded Stata) which I think could provide help with your analysis. Near the end of the article he cautions against the use of arbitrary numbers that are close to zero, such as 0.01, 0.0001, 0.0000001, and 0 since in logs they are -4... | How small a quantity should be added to x to avoid taking the log of zero? | @miura
I came across this article by Bill Gould on the Stata blog (I think he actually founded Stata) which I think could provide help with your analysis. Near the end of the article he cautions again | How small a quantity should be added to x to avoid taking the log of zero?
@miura
I came across this article by Bill Gould on the Stata blog (I think he actually founded Stata) which I think could provide help with your analysis. Near the end of the article he cautions against the use of arbitrary numbers that are clos... | How small a quantity should be added to x to avoid taking the log of zero?
@miura
I came across this article by Bill Gould on the Stata blog (I think he actually founded Stata) which I think could provide help with your analysis. Near the end of the article he cautions again |
3,062 | How small a quantity should be added to x to avoid taking the log of zero? | To clarify how to deal with the log of zero in regression models, we have written a pedagogical paper explaining the best solution and the common mistakes people make in practice. We also came out with a new solution to tackle this issue.
You can find the paper by clicking here: https://ssrn.com/abstract=3444996
First... | How small a quantity should be added to x to avoid taking the log of zero? | To clarify how to deal with the log of zero in regression models, we have written a pedagogical paper explaining the best solution and the common mistakes people make in practice. We also came out wit | How small a quantity should be added to x to avoid taking the log of zero?
To clarify how to deal with the log of zero in regression models, we have written a pedagogical paper explaining the best solution and the common mistakes people make in practice. We also came out with a new solution to tackle this issue.
You ca... | How small a quantity should be added to x to avoid taking the log of zero?
To clarify how to deal with the log of zero in regression models, we have written a pedagogical paper explaining the best solution and the common mistakes people make in practice. We also came out wit |
3,063 | How small a quantity should be added to x to avoid taking the log of zero? | You can set the zeros of the $i^{th}$ variable to the ${\rm mean}(x_i) - n\times{\rm stddev}(x_i)$ where $n$ is large enough to distinguish these cases from the rest (e.g., 6 or 10).
Note that any such artificial setup will affect your analyses so you should be careful with your interpretation and in some cases discar... | How small a quantity should be added to x to avoid taking the log of zero? | You can set the zeros of the $i^{th}$ variable to the ${\rm mean}(x_i) - n\times{\rm stddev}(x_i)$ where $n$ is large enough to distinguish these cases from the rest (e.g., 6 or 10).
Note that any su | How small a quantity should be added to x to avoid taking the log of zero?
You can set the zeros of the $i^{th}$ variable to the ${\rm mean}(x_i) - n\times{\rm stddev}(x_i)$ where $n$ is large enough to distinguish these cases from the rest (e.g., 6 or 10).
Note that any such artificial setup will affect your analyses... | How small a quantity should be added to x to avoid taking the log of zero?
You can set the zeros of the $i^{th}$ variable to the ${\rm mean}(x_i) - n\times{\rm stddev}(x_i)$ where $n$ is large enough to distinguish these cases from the rest (e.g., 6 or 10).
Note that any su |
3,064 | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)? | Normally I wouldn't report $R^2$ at all. Hosmer and Lemeshow, in their textbook Applied Logistic Regression (2nd Ed.), explain why:
In general, [$R^2$ measures] are based on various comparisons of the predicted values from the fitted model to those from [the base model], the no data or intercept only model and, as a ... | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)? | Normally I wouldn't report $R^2$ at all. Hosmer and Lemeshow, in their textbook Applied Logistic Regression (2nd Ed.), explain why:
In general, [$R^2$ measures] are based on various comparisons of t | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)?
Normally I wouldn't report $R^2$ at all. Hosmer and Lemeshow, in their textbook Applied Logistic Regression (2nd Ed.), explain why:
In general, [$R^2$ measures] are based on various comparisons of the predicted values... | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)?
Normally I wouldn't report $R^2$ at all. Hosmer and Lemeshow, in their textbook Applied Logistic Regression (2nd Ed.), explain why:
In general, [$R^2$ measures] are based on various comparisons of t |
3,065 | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)? | Both indices are measures of strength of association (i.e. whether any predictor is associated with the outcome, as for an LR test), and can be used to quantify predictive ability or model performance. A single predictor may have a significant effect on the outcome but it might not necessarily be so useful for predicti... | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)? | Both indices are measures of strength of association (i.e. whether any predictor is associated with the outcome, as for an LR test), and can be used to quantify predictive ability or model performance | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)?
Both indices are measures of strength of association (i.e. whether any predictor is associated with the outcome, as for an LR test), and can be used to quantify predictive ability or model performance. A single predicto... | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)?
Both indices are measures of strength of association (i.e. whether any predictor is associated with the outcome, as for an LR test), and can be used to quantify predictive ability or model performance |
3,066 | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)? | I would have thought the main problem with any kind of $R^2$ measure for logistic regression is that you are dealing with a model which has a known noise value. This is unlike standard linear regression, where the noise level is usually treated as unknown. For we can write a glm probability density function as:
$$f(y... | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)? | I would have thought the main problem with any kind of $R^2$ measure for logistic regression is that you are dealing with a model which has a known noise value. This is unlike standard linear regress | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)?
I would have thought the main problem with any kind of $R^2$ measure for logistic regression is that you are dealing with a model which has a known noise value. This is unlike standard linear regression, where the nois... | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)?
I would have thought the main problem with any kind of $R^2$ measure for logistic regression is that you are dealing with a model which has a known noise value. This is unlike standard linear regress |
3,067 | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)? | I found Tue Tjur's short paper "Coefficients of Determination in Logistic Regression Models - A New Proposal: The Coefficient of Discrimination" (2009,
The American Statistician) on various proposals for a coefficient of determination in logistic models quite enlightening. He does a good job highlighting pros and cons... | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)? | I found Tue Tjur's short paper "Coefficients of Determination in Logistic Regression Models - A New Proposal: The Coefficient of Discrimination" (2009,
The American Statistician) on various proposals | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)?
I found Tue Tjur's short paper "Coefficients of Determination in Logistic Regression Models - A New Proposal: The Coefficient of Discrimination" (2009,
The American Statistician) on various proposals for a coefficient ... | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)?
I found Tue Tjur's short paper "Coefficients of Determination in Logistic Regression Models - A New Proposal: The Coefficient of Discrimination" (2009,
The American Statistician) on various proposals |
3,068 | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)? | I was also going to say 'neither of them', so i've upvoted whuber's answer.
As well as criticising R^2, Hosmer & Lemeshow did propose an alternative measure of goodness-of-fit for logistic regression that is sometimes useful. This is based on dividing the data into (say) 10 groups of equal size (or as near as possible)... | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)? | I was also going to say 'neither of them', so i've upvoted whuber's answer.
As well as criticising R^2, Hosmer & Lemeshow did propose an alternative measure of goodness-of-fit for logistic regression | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)?
I was also going to say 'neither of them', so i've upvoted whuber's answer.
As well as criticising R^2, Hosmer & Lemeshow did propose an alternative measure of goodness-of-fit for logistic regression that is sometimes u... | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)?
I was also going to say 'neither of them', so i've upvoted whuber's answer.
As well as criticising R^2, Hosmer & Lemeshow did propose an alternative measure of goodness-of-fit for logistic regression |
3,069 | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)? | Despite the arguments against using pseudo-r-squareds, some people will for various reasons want to continue using them at least at certain times. What I have internalized from my readings (and I'm sorry I cannot provide citations at the moment) is that
if both C&S and Nag. are below .5, C&S will be a better gauge;
... | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)? | Despite the arguments against using pseudo-r-squareds, some people will for various reasons want to continue using them at least at certain times. What I have internalized from my readings (and I'm s | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)?
Despite the arguments against using pseudo-r-squareds, some people will for various reasons want to continue using them at least at certain times. What I have internalized from my readings (and I'm sorry I cannot provi... | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)?
Despite the arguments against using pseudo-r-squareds, some people will for various reasons want to continue using them at least at certain times. What I have internalized from my readings (and I'm s |
3,070 | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)? | I would agree in general that just using R2 is not good. But also see that point of @rolando2 comments that focusing on classification metrics could be not enough while comparing the models.
I guess my contribution to the discussion is that I think that several measures are to be reported to assess different model qual... | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)? | I would agree in general that just using R2 is not good. But also see that point of @rolando2 comments that focusing on classification metrics could be not enough while comparing the models.
I guess m | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)?
I would agree in general that just using R2 is not good. But also see that point of @rolando2 comments that focusing on classification metrics could be not enough while comparing the models.
I guess my contribution to t... | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)?
I would agree in general that just using R2 is not good. But also see that point of @rolando2 comments that focusing on classification metrics could be not enough while comparing the models.
I guess m |
3,071 | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)? | I would prefer the Nagelkerke as this model fit attains 1 when the model fits perfectly giving the reader a sense of how far your model is from perfect fit. The Cox & Shell does not attain 1 for perfect model fit and hence interpreting a value of 0.09 is a bit harder. See this url for further info on Pseudo RSquared fo... | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)? | I would prefer the Nagelkerke as this model fit attains 1 when the model fits perfectly giving the reader a sense of how far your model is from perfect fit. The Cox & Shell does not attain 1 for perfe | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)?
I would prefer the Nagelkerke as this model fit attains 1 when the model fits perfectly giving the reader a sense of how far your model is from perfect fit. The Cox & Shell does not attain 1 for perfect model fit and he... | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)?
I would prefer the Nagelkerke as this model fit attains 1 when the model fits perfectly giving the reader a sense of how far your model is from perfect fit. The Cox & Shell does not attain 1 for perfe |
3,072 | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)? | Instead of the Nagelkerke way of scaling $R^2$ to allow a 1.0 to be attained, I prefer to substitute the effective sample size for $N$ in the $R^2$ formula. This will not reach 1.0 for perfect binary predictions but this approach translates to other settings such as survival analysis where often the effective $N$ is t... | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)? | Instead of the Nagelkerke way of scaling $R^2$ to allow a 1.0 to be attained, I prefer to substitute the effective sample size for $N$ in the $R^2$ formula. This will not reach 1.0 for perfect binary | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)?
Instead of the Nagelkerke way of scaling $R^2$ to allow a 1.0 to be attained, I prefer to substitute the effective sample size for $N$ in the $R^2$ formula. This will not reach 1.0 for perfect binary predictions but th... | Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)?
Instead of the Nagelkerke way of scaling $R^2$ to allow a 1.0 to be attained, I prefer to substitute the effective sample size for $N$ in the $R^2$ formula. This will not reach 1.0 for perfect binary |
3,073 | Should I use a categorical cross-entropy or binary cross-entropy loss for binary predictions? | Bernoulli$^*$ cross-entropy loss is a special case of categorical cross-entropy loss for $m=2$.
$$
\begin{align}
\mathcal{L}(\theta)
&= -\frac{1}{n}\sum_{i=1}^n\sum_{j=1}^m y_{ij}\log(p_{ij}) \\
&= -\frac{1}{n}\sum_{i=1}^n \left[y_i \log(p_i) + (1-y_i) \log(1-p_i)\right]
\end{align}
$$
Where $i$ indexes samples/observ... | Should I use a categorical cross-entropy or binary cross-entropy loss for binary predictions? | Bernoulli$^*$ cross-entropy loss is a special case of categorical cross-entropy loss for $m=2$.
$$
\begin{align}
\mathcal{L}(\theta)
&= -\frac{1}{n}\sum_{i=1}^n\sum_{j=1}^m y_{ij}\log(p_{ij}) \\
&= -\ | Should I use a categorical cross-entropy or binary cross-entropy loss for binary predictions?
Bernoulli$^*$ cross-entropy loss is a special case of categorical cross-entropy loss for $m=2$.
$$
\begin{align}
\mathcal{L}(\theta)
&= -\frac{1}{n}\sum_{i=1}^n\sum_{j=1}^m y_{ij}\log(p_{ij}) \\
&= -\frac{1}{n}\sum_{i=1}^n \le... | Should I use a categorical cross-entropy or binary cross-entropy loss for binary predictions?
Bernoulli$^*$ cross-entropy loss is a special case of categorical cross-entropy loss for $m=2$.
$$
\begin{align}
\mathcal{L}(\theta)
&= -\frac{1}{n}\sum_{i=1}^n\sum_{j=1}^m y_{ij}\log(p_{ij}) \\
&= -\ |
3,074 | Should I use a categorical cross-entropy or binary cross-entropy loss for binary predictions? | There are three kinds of classification tasks:
Binary classification: two exclusive classes
Multi-class classification: more than two exclusive classes
Multi-label classification: just non-exclusive classes
Here, we can say
In the case of (1), you need to use binary cross entropy.
In the case of (2), you nee... | Should I use a categorical cross-entropy or binary cross-entropy loss for binary predictions? | There are three kinds of classification tasks:
Binary classification: two exclusive classes
Multi-class classification: more than two exclusive classes
Multi-label classification: just non-excl | Should I use a categorical cross-entropy or binary cross-entropy loss for binary predictions?
There are three kinds of classification tasks:
Binary classification: two exclusive classes
Multi-class classification: more than two exclusive classes
Multi-label classification: just non-exclusive classes
Here, we ca... | Should I use a categorical cross-entropy or binary cross-entropy loss for binary predictions?
There are three kinds of classification tasks:
Binary classification: two exclusive classes
Multi-class classification: more than two exclusive classes
Multi-label classification: just non-excl |
3,075 | Should I use a categorical cross-entropy or binary cross-entropy loss for binary predictions? | Want to improve this post? Add citations from reputable sources by editing the post. Posts with unsourced content may be edited or deleted.
Binary cross-entropy is for multi-label classifications, whereas categorical cross entropy is for multi-class ... | Should I use a categorical cross-entropy or binary cross-entropy loss for binary predictions? | Want to improve this post? Add citations from reputable sources by editing the post. Posts with unsourced content may be edited or deleted.
| Should I use a categorical cross-entropy or binary cross-entropy loss for binary predictions?
Want to improve this post? Add citations from reputable sources by editing the post. Posts with unsourced content may be edited or deleted.
Binary cross-ent... | Should I use a categorical cross-entropy or binary cross-entropy loss for binary predictions?
Want to improve this post? Add citations from reputable sources by editing the post. Posts with unsourced content may be edited or deleted.
|
3,076 | Should I use a categorical cross-entropy or binary cross-entropy loss for binary predictions? | Binary Cross Entropy is a special case of Categorical Cross Entropy with 2 classes (class=1, and class=0). If we formulate Binary Cross Entropy this way, then we can use the general Cross-Entropy loss formula here: Sum(y*log y) for each class. Notice how this is the same as binary cross entropy.
For multi-label class... | Should I use a categorical cross-entropy or binary cross-entropy loss for binary predictions? | Binary Cross Entropy is a special case of Categorical Cross Entropy with 2 classes (class=1, and class=0). If we formulate Binary Cross Entropy this way, then we can use the general Cross-Entropy los | Should I use a categorical cross-entropy or binary cross-entropy loss for binary predictions?
Binary Cross Entropy is a special case of Categorical Cross Entropy with 2 classes (class=1, and class=0). If we formulate Binary Cross Entropy this way, then we can use the general Cross-Entropy loss formula here: Sum(y*log ... | Should I use a categorical cross-entropy or binary cross-entropy loss for binary predictions?
Binary Cross Entropy is a special case of Categorical Cross Entropy with 2 classes (class=1, and class=0). If we formulate Binary Cross Entropy this way, then we can use the general Cross-Entropy los |
3,077 | How to tune hyperparameters of xgboost trees? | Since the interface to xgboost in caret has recently changed, here is a script that provides a fully commented walkthrough of using caret to tune xgboost hyper-parameters.
For this, I will be using the training data from the Kaggle competition "Give Me Some Credit".
1. Fitting an xgboost model
In this section, we: ... | How to tune hyperparameters of xgboost trees? | Since the interface to xgboost in caret has recently changed, here is a script that provides a fully commented walkthrough of using caret to tune xgboost hyper-parameters.
For this, I will be using t | How to tune hyperparameters of xgboost trees?
Since the interface to xgboost in caret has recently changed, here is a script that provides a fully commented walkthrough of using caret to tune xgboost hyper-parameters.
For this, I will be using the training data from the Kaggle competition "Give Me Some Credit".
1. Fit... | How to tune hyperparameters of xgboost trees?
Since the interface to xgboost in caret has recently changed, here is a script that provides a fully commented walkthrough of using caret to tune xgboost hyper-parameters.
For this, I will be using t |
3,078 | How to tune hyperparameters of xgboost trees? | Caret package have incorporated xgboost.
cv.ctrl <- trainControl(method = "repeatedcv", repeats = 1,number = 3,
#summaryFunction = twoClassSummary,
classProbs = TRUE,
allowParallel=T)
xgb.grid <- expand.grid(nrounds = 1000,
... | How to tune hyperparameters of xgboost trees? | Caret package have incorporated xgboost.
cv.ctrl <- trainControl(method = "repeatedcv", repeats = 1,number = 3,
#summaryFunction = twoClassSummary,
cla | How to tune hyperparameters of xgboost trees?
Caret package have incorporated xgboost.
cv.ctrl <- trainControl(method = "repeatedcv", repeats = 1,number = 3,
#summaryFunction = twoClassSummary,
classProbs = TRUE,
allowParallel=T)
xgb.grid <- ... | How to tune hyperparameters of xgboost trees?
Caret package have incorporated xgboost.
cv.ctrl <- trainControl(method = "repeatedcv", repeats = 1,number = 3,
#summaryFunction = twoClassSummary,
cla |
3,079 | How to tune hyperparameters of xgboost trees? | I know this is an old question, but I use a different method from the ones above. I use the BayesianOptimization function from the Bayesian Optimization package to find optimal parameters. To do this, you first create cross validation folds, then create a function xgb.cv.bayes that has as parameters the boosting hype... | How to tune hyperparameters of xgboost trees? | I know this is an old question, but I use a different method from the ones above. I use the BayesianOptimization function from the Bayesian Optimization package to find optimal parameters. To do thi | How to tune hyperparameters of xgboost trees?
I know this is an old question, but I use a different method from the ones above. I use the BayesianOptimization function from the Bayesian Optimization package to find optimal parameters. To do this, you first create cross validation folds, then create a function xgb.cv.... | How to tune hyperparameters of xgboost trees?
I know this is an old question, but I use a different method from the ones above. I use the BayesianOptimization function from the Bayesian Optimization package to find optimal parameters. To do thi |
3,080 | How to tune hyperparameters of xgboost trees? | This is an older question but thought I would share how I tune xgboost parameters. I originally thought I would use caret for this but recently found an issue handling all of the parameters as well as missing values. I was also considering writing an iterating loop through different combinations of parameters but want... | How to tune hyperparameters of xgboost trees? | This is an older question but thought I would share how I tune xgboost parameters. I originally thought I would use caret for this but recently found an issue handling all of the parameters as well as | How to tune hyperparameters of xgboost trees?
This is an older question but thought I would share how I tune xgboost parameters. I originally thought I would use caret for this but recently found an issue handling all of the parameters as well as missing values. I was also considering writing an iterating loop through... | How to tune hyperparameters of xgboost trees?
This is an older question but thought I would share how I tune xgboost parameters. I originally thought I would use caret for this but recently found an issue handling all of the parameters as well as |
3,081 | Why do we minimize the negative likelihood if it is equivalent to maximization of the likelihood? | This is an alternative answer: optimizers in statistical packages usually work by minimizing the result of a function. If your function gives the likelihood value first it's more convenient to use logarithm in order to decrease the value returned by likelihood function. Then, since the log likelihood and likelihood fun... | Why do we minimize the negative likelihood if it is equivalent to maximization of the likelihood? | This is an alternative answer: optimizers in statistical packages usually work by minimizing the result of a function. If your function gives the likelihood value first it's more convenient to use log | Why do we minimize the negative likelihood if it is equivalent to maximization of the likelihood?
This is an alternative answer: optimizers in statistical packages usually work by minimizing the result of a function. If your function gives the likelihood value first it's more convenient to use logarithm in order to dec... | Why do we minimize the negative likelihood if it is equivalent to maximization of the likelihood?
This is an alternative answer: optimizers in statistical packages usually work by minimizing the result of a function. If your function gives the likelihood value first it's more convenient to use log |
3,082 | Why do we minimize the negative likelihood if it is equivalent to maximization of the likelihood? | Optimisers typically minimize a function, so we use negative log-likelihood as minimising that is equivalent to maximising the log-likelihood or the likelihood itself.
Just for completeness, I would mention that the logarithm is a monotonic function, so optimising a function is the same as optimising the logarithm of i... | Why do we minimize the negative likelihood if it is equivalent to maximization of the likelihood? | Optimisers typically minimize a function, so we use negative log-likelihood as minimising that is equivalent to maximising the log-likelihood or the likelihood itself.
Just for completeness, I would m | Why do we minimize the negative likelihood if it is equivalent to maximization of the likelihood?
Optimisers typically minimize a function, so we use negative log-likelihood as minimising that is equivalent to maximising the log-likelihood or the likelihood itself.
Just for completeness, I would mention that the logari... | Why do we minimize the negative likelihood if it is equivalent to maximization of the likelihood?
Optimisers typically minimize a function, so we use negative log-likelihood as minimising that is equivalent to maximising the log-likelihood or the likelihood itself.
Just for completeness, I would m |
3,083 | Why do we minimize the negative likelihood if it is equivalent to maximization of the likelihood? | Here minimizing means decrease the distance of two distributions to its lowest: the target Bernoulli distribution and the generated result distribution. We measure the distance of two distributions using Kullback-Leibler divergence(also called relative entropy), and due to the large number theory minimizing KL divergen... | Why do we minimize the negative likelihood if it is equivalent to maximization of the likelihood? | Here minimizing means decrease the distance of two distributions to its lowest: the target Bernoulli distribution and the generated result distribution. We measure the distance of two distributions us | Why do we minimize the negative likelihood if it is equivalent to maximization of the likelihood?
Here minimizing means decrease the distance of two distributions to its lowest: the target Bernoulli distribution and the generated result distribution. We measure the distance of two distributions using Kullback-Leibler d... | Why do we minimize the negative likelihood if it is equivalent to maximization of the likelihood?
Here minimizing means decrease the distance of two distributions to its lowest: the target Bernoulli distribution and the generated result distribution. We measure the distance of two distributions us |
3,084 | Why do we minimize the negative likelihood if it is equivalent to maximization of the likelihood? | The answer is simpler than you might think. It is the convention that we call the optimization objective function a "cost function" or "loss function" and therefore, we want to minimize them, rather than maximize them, and hence the negative log likelihood is formed, rather than
positive likelihood in your word. Techn... | Why do we minimize the negative likelihood if it is equivalent to maximization of the likelihood? | The answer is simpler than you might think. It is the convention that we call the optimization objective function a "cost function" or "loss function" and therefore, we want to minimize them, rather t | Why do we minimize the negative likelihood if it is equivalent to maximization of the likelihood?
The answer is simpler than you might think. It is the convention that we call the optimization objective function a "cost function" or "loss function" and therefore, we want to minimize them, rather than maximize them, and... | Why do we minimize the negative likelihood if it is equivalent to maximization of the likelihood?
The answer is simpler than you might think. It is the convention that we call the optimization objective function a "cost function" or "loss function" and therefore, we want to minimize them, rather t |
3,085 | Why do we minimize the negative likelihood if it is equivalent to maximization of the likelihood? | The main reason for using log is to handle very small likelihoods. A 32-bit float can only go down to 2^-126 before it gets rounded to 0. It's not just because optimizers are built to minimize functions, since you can easily minimize -likelihood. If you have a large model computing likelihood of a sequence with hundre... | Why do we minimize the negative likelihood if it is equivalent to maximization of the likelihood? | The main reason for using log is to handle very small likelihoods. A 32-bit float can only go down to 2^-126 before it gets rounded to 0. It's not just because optimizers are built to minimize functio | Why do we minimize the negative likelihood if it is equivalent to maximization of the likelihood?
The main reason for using log is to handle very small likelihoods. A 32-bit float can only go down to 2^-126 before it gets rounded to 0. It's not just because optimizers are built to minimize functions, since you can easi... | Why do we minimize the negative likelihood if it is equivalent to maximization of the likelihood?
The main reason for using log is to handle very small likelihoods. A 32-bit float can only go down to 2^-126 before it gets rounded to 0. It's not just because optimizers are built to minimize functio |
3,086 | Why is the Jeffreys prior useful? | Let me complete Zen's answer. I don't very like the notion of "representing ignorance". The important thing is not the Jeffreys prior but the Jeffreys posterior. This posterior aims to reflect as best as possible the information about the parameters brought by the data. The invariance property is naturally required for... | Why is the Jeffreys prior useful? | Let me complete Zen's answer. I don't very like the notion of "representing ignorance". The important thing is not the Jeffreys prior but the Jeffreys posterior. This posterior aims to reflect as best | Why is the Jeffreys prior useful?
Let me complete Zen's answer. I don't very like the notion of "representing ignorance". The important thing is not the Jeffreys prior but the Jeffreys posterior. This posterior aims to reflect as best as possible the information about the parameters brought by the data. The invariance ... | Why is the Jeffreys prior useful?
Let me complete Zen's answer. I don't very like the notion of "representing ignorance". The important thing is not the Jeffreys prior but the Jeffreys posterior. This posterior aims to reflect as best |
3,087 | Why is the Jeffreys prior useful? | Suppose that you and a friend are analyzing the same set of data using a normal model. You adopt the usual parameterization of the normal model using the mean and the variance as parameters, but your friend prefers to parameterize the normal model with the coefficient of variation and the precision as parameters (which... | Why is the Jeffreys prior useful? | Suppose that you and a friend are analyzing the same set of data using a normal model. You adopt the usual parameterization of the normal model using the mean and the variance as parameters, but your | Why is the Jeffreys prior useful?
Suppose that you and a friend are analyzing the same set of data using a normal model. You adopt the usual parameterization of the normal model using the mean and the variance as parameters, but your friend prefers to parameterize the normal model with the coefficient of variation and ... | Why is the Jeffreys prior useful?
Suppose that you and a friend are analyzing the same set of data using a normal model. You adopt the usual parameterization of the normal model using the mean and the variance as parameters, but your |
3,088 | Why is the Jeffreys prior useful? | To add some quotations to Zen's great answer: According to Jaynes, the Jeffreys prior is an example of the principle of transformation groups, which results from the principle of indifference:
The essence of the principle is just: (1) we recognize that a
probability assignment is a means of describing a certain stat... | Why is the Jeffreys prior useful? | To add some quotations to Zen's great answer: According to Jaynes, the Jeffreys prior is an example of the principle of transformation groups, which results from the principle of indifference:
The es | Why is the Jeffreys prior useful?
To add some quotations to Zen's great answer: According to Jaynes, the Jeffreys prior is an example of the principle of transformation groups, which results from the principle of indifference:
The essence of the principle is just: (1) we recognize that a
probability assignment is a ... | Why is the Jeffreys prior useful?
To add some quotations to Zen's great answer: According to Jaynes, the Jeffreys prior is an example of the principle of transformation groups, which results from the principle of indifference:
The es |
3,089 | Why is the Jeffreys prior useful? | While often of interest, if only for setting a reference prior against which to gauge other priors, Jeffreys priors may be completely useless as for instance when they lead to improper posteriors: this is for instance the case with the simple two-component Gaussian mixture
$$p\mathcal{N}(\mu_0,\sigma_0^2)+(1-p)\mathcal... | Why is the Jeffreys prior useful? | While often of interest, if only for setting a reference prior against which to gauge other priors, Jeffreys priors may be completely useless as for instance when they lead to improper posteriors: thi | Why is the Jeffreys prior useful?
While often of interest, if only for setting a reference prior against which to gauge other priors, Jeffreys priors may be completely useless as for instance when they lead to improper posteriors: this is for instance the case with the simple two-component Gaussian mixture
$$p\mathcal{... | Why is the Jeffreys prior useful?
While often of interest, if only for setting a reference prior against which to gauge other priors, Jeffreys priors may be completely useless as for instance when they lead to improper posteriors: thi |
3,090 | Why is the Jeffreys prior useful? | Jeffreys prior is useless. This is because:
It just specifies the form of the distribution; it does not tell you what its parameters should be.
You are never completely ignorant - there is always something about the parameter that you know (e.g. often it cannot be infinity). Use it for your inference by defining a pri... | Why is the Jeffreys prior useful? | Jeffreys prior is useless. This is because:
It just specifies the form of the distribution; it does not tell you what its parameters should be.
You are never completely ignorant - there is always som | Why is the Jeffreys prior useful?
Jeffreys prior is useless. This is because:
It just specifies the form of the distribution; it does not tell you what its parameters should be.
You are never completely ignorant - there is always something about the parameter that you know (e.g. often it cannot be infinity). Use it fo... | Why is the Jeffreys prior useful?
Jeffreys prior is useless. This is because:
It just specifies the form of the distribution; it does not tell you what its parameters should be.
You are never completely ignorant - there is always som |
3,091 | Criticism of Pearl's theory of causality | Some authors dislike Pearl's focus on the directed acyclic graph (DAG) as the way in which to view causality. Pearl essentially argues that any causal system can be considered as a non-parametric structural equation model (NPSEM), in which the value of each node is taken as a function of its parents and some individua... | Criticism of Pearl's theory of causality | Some authors dislike Pearl's focus on the directed acyclic graph (DAG) as the way in which to view causality. Pearl essentially argues that any causal system can be considered as a non-parametric str | Criticism of Pearl's theory of causality
Some authors dislike Pearl's focus on the directed acyclic graph (DAG) as the way in which to view causality. Pearl essentially argues that any causal system can be considered as a non-parametric structural equation model (NPSEM), in which the value of each node is taken as a f... | Criticism of Pearl's theory of causality
Some authors dislike Pearl's focus on the directed acyclic graph (DAG) as the way in which to view causality. Pearl essentially argues that any causal system can be considered as a non-parametric str |
3,092 | Criticism of Pearl's theory of causality | I think this framework has a lot of trouble with general equilibrium effects or Stable Unit Treatment Value Assumption violations. In that case, the "untreated" observations no longer provide the desired counterfactual in a meaningful way. Massive job training programs that shift the entire wage distribution are one ex... | Criticism of Pearl's theory of causality | I think this framework has a lot of trouble with general equilibrium effects or Stable Unit Treatment Value Assumption violations. In that case, the "untreated" observations no longer provide the desi | Criticism of Pearl's theory of causality
I think this framework has a lot of trouble with general equilibrium effects or Stable Unit Treatment Value Assumption violations. In that case, the "untreated" observations no longer provide the desired counterfactual in a meaningful way. Massive job training programs that shif... | Criticism of Pearl's theory of causality
I think this framework has a lot of trouble with general equilibrium effects or Stable Unit Treatment Value Assumption violations. In that case, the "untreated" observations no longer provide the desi |
3,093 | Criticism of Pearl's theory of causality | Counterfactual formal causal reasoning of the form motivated by Pearl is ill-suited to the analysis of complex dynamic causal systems (i.e. networks in which every variable is either directly or indirectly the cause of every variable at some future time).1
Disclaimer 1: I am a fan of Pearl's framework, and had the priv... | Criticism of Pearl's theory of causality | Counterfactual formal causal reasoning of the form motivated by Pearl is ill-suited to the analysis of complex dynamic causal systems (i.e. networks in which every variable is either directly or indir | Criticism of Pearl's theory of causality
Counterfactual formal causal reasoning of the form motivated by Pearl is ill-suited to the analysis of complex dynamic causal systems (i.e. networks in which every variable is either directly or indirectly the cause of every variable at some future time).1
Disclaimer 1: I am a f... | Criticism of Pearl's theory of causality
Counterfactual formal causal reasoning of the form motivated by Pearl is ill-suited to the analysis of complex dynamic causal systems (i.e. networks in which every variable is either directly or indir |
3,094 | Criticism of Pearl's theory of causality | The most important criticism of Pearl's system is, from my perspective, that it has not yielded any practical, empirical advances anywhere it has been used. Given how long it has been around, there's no reason to think it will ever be a practical tool. This indicates that it can be used for some theoretical and perhaps... | Criticism of Pearl's theory of causality | The most important criticism of Pearl's system is, from my perspective, that it has not yielded any practical, empirical advances anywhere it has been used. Given how long it has been around, there's | Criticism of Pearl's theory of causality
The most important criticism of Pearl's system is, from my perspective, that it has not yielded any practical, empirical advances anywhere it has been used. Given how long it has been around, there's no reason to think it will ever be a practical tool. This indicates that it can... | Criticism of Pearl's theory of causality
The most important criticism of Pearl's system is, from my perspective, that it has not yielded any practical, empirical advances anywhere it has been used. Given how long it has been around, there's |
3,095 | Criticism of Pearl's theory of causality | Reading answers and comments I feel the opportunity to add something.
The accepted answer, by rje42, is focused on DAG’s and non-parametric systems; strongly related concepts. Now, capabilities and limitations of these tools can be argued, however we have to say that linear SEMs are part of the Theory presented in Pear... | Criticism of Pearl's theory of causality | Reading answers and comments I feel the opportunity to add something.
The accepted answer, by rje42, is focused on DAG’s and non-parametric systems; strongly related concepts. Now, capabilities and li | Criticism of Pearl's theory of causality
Reading answers and comments I feel the opportunity to add something.
The accepted answer, by rje42, is focused on DAG’s and non-parametric systems; strongly related concepts. Now, capabilities and limitations of these tools can be argued, however we have to say that linear SEMs... | Criticism of Pearl's theory of causality
Reading answers and comments I feel the opportunity to add something.
The accepted answer, by rje42, is focused on DAG’s and non-parametric systems; strongly related concepts. Now, capabilities and li |
3,096 | Criticism of Pearl's theory of causality | The synthesis behind causal reasoning is access to different aspects of reality/model using inferences from data. The paradoxical features in a model are avoided through formalisation in mathematical language. Informally, these counterintuitive features arise in observing a model unlike intervening through doing calcul... | Criticism of Pearl's theory of causality | The synthesis behind causal reasoning is access to different aspects of reality/model using inferences from data. The paradoxical features in a model are avoided through formalisation in mathematical | Criticism of Pearl's theory of causality
The synthesis behind causal reasoning is access to different aspects of reality/model using inferences from data. The paradoxical features in a model are avoided through formalisation in mathematical language. Informally, these counterintuitive features arise in observing a mode... | Criticism of Pearl's theory of causality
The synthesis behind causal reasoning is access to different aspects of reality/model using inferences from data. The paradoxical features in a model are avoided through formalisation in mathematical |
3,097 | Assumptions regarding bootstrap estimates of uncertainty | There are several ways that one can conceivably apply the bootstrap. The two most basic approaches are what are deemed the "nonparametric" and "parametric" bootstrap. The second one assumes that the model you're using is (essentially) correct.
Let's focus on the first one. We'll assume that you have a random sample $X_... | Assumptions regarding bootstrap estimates of uncertainty | There are several ways that one can conceivably apply the bootstrap. The two most basic approaches are what are deemed the "nonparametric" and "parametric" bootstrap. The second one assumes that the m | Assumptions regarding bootstrap estimates of uncertainty
There are several ways that one can conceivably apply the bootstrap. The two most basic approaches are what are deemed the "nonparametric" and "parametric" bootstrap. The second one assumes that the model you're using is (essentially) correct.
Let's focus on the ... | Assumptions regarding bootstrap estimates of uncertainty
There are several ways that one can conceivably apply the bootstrap. The two most basic approaches are what are deemed the "nonparametric" and "parametric" bootstrap. The second one assumes that the m |
3,098 | Assumptions regarding bootstrap estimates of uncertainty | Here is a different approach to thinking about it:
Start with the theory where we know the true distribution, we can discover properties of sample statistics by simulating from the true distribution. This is how Gosset developed the t-distribution and t-test, by sampling from known normals and computing the statistic.... | Assumptions regarding bootstrap estimates of uncertainty | Here is a different approach to thinking about it:
Start with the theory where we know the true distribution, we can discover properties of sample statistics by simulating from the true distribution. | Assumptions regarding bootstrap estimates of uncertainty
Here is a different approach to thinking about it:
Start with the theory where we know the true distribution, we can discover properties of sample statistics by simulating from the true distribution. This is how Gosset developed the t-distribution and t-test, by... | Assumptions regarding bootstrap estimates of uncertainty
Here is a different approach to thinking about it:
Start with the theory where we know the true distribution, we can discover properties of sample statistics by simulating from the true distribution. |
3,099 | Assumptions regarding bootstrap estimates of uncertainty | The main trick (and sting) of bootstrapping is that it is an asymptotic theory: if you have an infinite sample to start with, the empirical distribution is going to be so close to the actual distribution that the difference is negligible.
Unfortunately, bootstrapping is often applied in small sample sizes. The common f... | Assumptions regarding bootstrap estimates of uncertainty | The main trick (and sting) of bootstrapping is that it is an asymptotic theory: if you have an infinite sample to start with, the empirical distribution is going to be so close to the actual distribut | Assumptions regarding bootstrap estimates of uncertainty
The main trick (and sting) of bootstrapping is that it is an asymptotic theory: if you have an infinite sample to start with, the empirical distribution is going to be so close to the actual distribution that the difference is negligible.
Unfortunately, bootstrap... | Assumptions regarding bootstrap estimates of uncertainty
The main trick (and sting) of bootstrapping is that it is an asymptotic theory: if you have an infinite sample to start with, the empirical distribution is going to be so close to the actual distribut |
3,100 | Assumptions regarding bootstrap estimates of uncertainty | I would argue not from the perspective of "asymptotically, the empirical distribution will be close to the actual distribution" (which, of course, is very true), but from a "long run perspective". In other words, in any particular case, the empirical distribution derived by bootstrapping will be off (sometimes shifted ... | Assumptions regarding bootstrap estimates of uncertainty | I would argue not from the perspective of "asymptotically, the empirical distribution will be close to the actual distribution" (which, of course, is very true), but from a "long run perspective". In | Assumptions regarding bootstrap estimates of uncertainty
I would argue not from the perspective of "asymptotically, the empirical distribution will be close to the actual distribution" (which, of course, is very true), but from a "long run perspective". In other words, in any particular case, the empirical distribution... | Assumptions regarding bootstrap estimates of uncertainty
I would argue not from the perspective of "asymptotically, the empirical distribution will be close to the actual distribution" (which, of course, is very true), but from a "long run perspective". In |
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