idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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7,001 | How to draw neat polygons around scatterplot regions in ggplot2 [closed] | If I understand your problem, you're looking for the convex hull of health and of unemployment. There are probably several packages to do this in R, one of which is package geometry. I'd imagine that the points are sorted in order around the perimeter, but you'd have to check that.
EDIT: Here's an example, which doesn'... | How to draw neat polygons around scatterplot regions in ggplot2 [closed] | If I understand your problem, you're looking for the convex hull of health and of unemployment. There are probably several packages to do this in R, one of which is package geometry. I'd imagine that | How to draw neat polygons around scatterplot regions in ggplot2 [closed]
If I understand your problem, you're looking for the convex hull of health and of unemployment. There are probably several packages to do this in R, one of which is package geometry. I'd imagine that the points are sorted in order around the perim... | How to draw neat polygons around scatterplot regions in ggplot2 [closed]
If I understand your problem, you're looking for the convex hull of health and of unemployment. There are probably several packages to do this in R, one of which is package geometry. I'd imagine that |
7,002 | How to draw neat polygons around scatterplot regions in ggplot2 [closed] | As of this afternoon, I've wrapped the chull function inside an R package as a geom_convexhull function.
Once the package is loaded, it can be used as any other geom, in your case it should be something like :
ggplot(d, aes(man, eff, colour=issue, fill=issue)) +
geom_convexhull(alpha=.5) +
geom_point() +
labs(... | How to draw neat polygons around scatterplot regions in ggplot2 [closed] | As of this afternoon, I've wrapped the chull function inside an R package as a geom_convexhull function.
Once the package is loaded, it can be used as any other geom, in your case it should be somethi | How to draw neat polygons around scatterplot regions in ggplot2 [closed]
As of this afternoon, I've wrapped the chull function inside an R package as a geom_convexhull function.
Once the package is loaded, it can be used as any other geom, in your case it should be something like :
ggplot(d, aes(man, eff, colour=issue,... | How to draw neat polygons around scatterplot regions in ggplot2 [closed]
As of this afternoon, I've wrapped the chull function inside an R package as a geom_convexhull function.
Once the package is loaded, it can be used as any other geom, in your case it should be somethi |
7,003 | Why is max pooling necessary in convolutional neural networks? | You can indeed do that, see Striving for Simplicity: The All Convolutional Net. Pooling gives you some amount of translation invariance, which may or may not be helpful. Also, pooling is faster to compute than convolutions. Still, you can always try replacing pooling by convolution with stride and see what works better... | Why is max pooling necessary in convolutional neural networks? | You can indeed do that, see Striving for Simplicity: The All Convolutional Net. Pooling gives you some amount of translation invariance, which may or may not be helpful. Also, pooling is faster to com | Why is max pooling necessary in convolutional neural networks?
You can indeed do that, see Striving for Simplicity: The All Convolutional Net. Pooling gives you some amount of translation invariance, which may or may not be helpful. Also, pooling is faster to compute than convolutions. Still, you can always try replaci... | Why is max pooling necessary in convolutional neural networks?
You can indeed do that, see Striving for Simplicity: The All Convolutional Net. Pooling gives you some amount of translation invariance, which may or may not be helpful. Also, pooling is faster to com |
7,004 | Why is max pooling necessary in convolutional neural networks? | Apparently max pooling helps because it extracts the sharpest features of an image. So given an image, the sharpest features are the best lower-level representation of an image. https://www.quora.com/What-is-the-benefit-of-using-average-pooling-rather-than-max-pooling
But according to Andrew Ng's Deep Learning lecture... | Why is max pooling necessary in convolutional neural networks? | Apparently max pooling helps because it extracts the sharpest features of an image. So given an image, the sharpest features are the best lower-level representation of an image. https://www.quora.com/ | Why is max pooling necessary in convolutional neural networks?
Apparently max pooling helps because it extracts the sharpest features of an image. So given an image, the sharpest features are the best lower-level representation of an image. https://www.quora.com/What-is-the-benefit-of-using-average-pooling-rather-than-... | Why is max pooling necessary in convolutional neural networks?
Apparently max pooling helps because it extracts the sharpest features of an image. So given an image, the sharpest features are the best lower-level representation of an image. https://www.quora.com/ |
7,005 | Why is max pooling necessary in convolutional neural networks? | Pooling mainly helps in extracting sharp and smooth features. It is also done to reduce variance and computations. Max-pooling helps in extracting low-level features like edges, points, etc. While Avg-pooling goes for smooth features.
If time constraint is not a problem, then one can skip the pooling layer and use a c... | Why is max pooling necessary in convolutional neural networks? | Pooling mainly helps in extracting sharp and smooth features. It is also done to reduce variance and computations. Max-pooling helps in extracting low-level features like edges, points, etc. While Avg | Why is max pooling necessary in convolutional neural networks?
Pooling mainly helps in extracting sharp and smooth features. It is also done to reduce variance and computations. Max-pooling helps in extracting low-level features like edges, points, etc. While Avg-pooling goes for smooth features.
If time constraint is... | Why is max pooling necessary in convolutional neural networks?
Pooling mainly helps in extracting sharp and smooth features. It is also done to reduce variance and computations. Max-pooling helps in extracting low-level features like edges, points, etc. While Avg |
7,006 | Why is max pooling necessary in convolutional neural networks? | It really depends on the images. In some scenarios, Max pooling can take away too much info, resulting in worst performance that a CNN without max pooling. See this video for a surprising comparison using the MNIST Fashion dataset: https://www.youtube.com/watch?v=0ixAwVAfejY | Why is max pooling necessary in convolutional neural networks? | It really depends on the images. In some scenarios, Max pooling can take away too much info, resulting in worst performance that a CNN without max pooling. See this video for a surprising comparison u | Why is max pooling necessary in convolutional neural networks?
It really depends on the images. In some scenarios, Max pooling can take away too much info, resulting in worst performance that a CNN without max pooling. See this video for a surprising comparison using the MNIST Fashion dataset: https://www.youtube.com/w... | Why is max pooling necessary in convolutional neural networks?
It really depends on the images. In some scenarios, Max pooling can take away too much info, resulting in worst performance that a CNN without max pooling. See this video for a surprising comparison u |
7,007 | Is a strong background in maths a total requisite for ML? | Stanford (Ng) and Caltech (Abu-Mostafa) have put machine learning classes on YouTube. You don't get to see the assignments, but the lectures don't rely on those. I recommend trying to watch those first, as those will help you to find out what math you need to learn. I believe a very similar class with assignments is ta... | Is a strong background in maths a total requisite for ML? | Stanford (Ng) and Caltech (Abu-Mostafa) have put machine learning classes on YouTube. You don't get to see the assignments, but the lectures don't rely on those. I recommend trying to watch those firs | Is a strong background in maths a total requisite for ML?
Stanford (Ng) and Caltech (Abu-Mostafa) have put machine learning classes on YouTube. You don't get to see the assignments, but the lectures don't rely on those. I recommend trying to watch those first, as those will help you to find out what math you need to le... | Is a strong background in maths a total requisite for ML?
Stanford (Ng) and Caltech (Abu-Mostafa) have put machine learning classes on YouTube. You don't get to see the assignments, but the lectures don't rely on those. I recommend trying to watch those firs |
7,008 | Is a strong background in maths a total requisite for ML? | Depending on the kind of application, you don't necessarily need a lot of math as a ML practitioner.
As a self-taught programmer (~15 years) and frequent college dropout without much background in math (Calculus III) or statistics, I started with machine learning / data mining with a few resources:
The book "Masteri... | Is a strong background in maths a total requisite for ML? | Depending on the kind of application, you don't necessarily need a lot of math as a ML practitioner.
As a self-taught programmer (~15 years) and frequent college dropout without much background in m | Is a strong background in maths a total requisite for ML?
Depending on the kind of application, you don't necessarily need a lot of math as a ML practitioner.
As a self-taught programmer (~15 years) and frequent college dropout without much background in math (Calculus III) or statistics, I started with machine learn... | Is a strong background in maths a total requisite for ML?
Depending on the kind of application, you don't necessarily need a lot of math as a ML practitioner.
As a self-taught programmer (~15 years) and frequent college dropout without much background in m |
7,009 | Is a strong background in maths a total requisite for ML? | I think this is a good question actually, and highly topical; I'm not sure if there is an answer however. A recent article stirred a deal of controversy (see here) by suggesting that data science was easy to learn online. One notable thing about most of the case studies mentioned in that article however is that they co... | Is a strong background in maths a total requisite for ML? | I think this is a good question actually, and highly topical; I'm not sure if there is an answer however. A recent article stirred a deal of controversy (see here) by suggesting that data science was | Is a strong background in maths a total requisite for ML?
I think this is a good question actually, and highly topical; I'm not sure if there is an answer however. A recent article stirred a deal of controversy (see here) by suggesting that data science was easy to learn online. One notable thing about most of the case... | Is a strong background in maths a total requisite for ML?
I think this is a good question actually, and highly topical; I'm not sure if there is an answer however. A recent article stirred a deal of controversy (see here) by suggesting that data science was |
7,010 | Is a strong background in maths a total requisite for ML? | Is a strong background in maths a total requisite for ML? – an answer and some speculation for ML conceptualized as being statistics ;-)
Around 1990 I had hopes for computer algebra being of assistance, I think it is but it is fairly limited. But it certainly helps with speeding up the learning of math (less need to de... | Is a strong background in maths a total requisite for ML? | Is a strong background in maths a total requisite for ML? – an answer and some speculation for ML conceptualized as being statistics ;-)
Around 1990 I had hopes for computer algebra being of assistanc | Is a strong background in maths a total requisite for ML?
Is a strong background in maths a total requisite for ML? – an answer and some speculation for ML conceptualized as being statistics ;-)
Around 1990 I had hopes for computer algebra being of assistance, I think it is but it is fairly limited. But it certainly he... | Is a strong background in maths a total requisite for ML?
Is a strong background in maths a total requisite for ML? – an answer and some speculation for ML conceptualized as being statistics ;-)
Around 1990 I had hopes for computer algebra being of assistanc |
7,011 | How to estimate shrinkage parameter in Lasso or ridge regression with >50K variables? | The function cv.glmnet from the R package glmnet does automatic cross-validation on a grid of $\lambda$ values used for $\ell_1$-penalized regression problems. In particular, for the lasso. The glmnet package also supports the more general elastic net penalty, which is a combination of $\ell_1$ and $\ell_2$ penalizatio... | How to estimate shrinkage parameter in Lasso or ridge regression with >50K variables? | The function cv.glmnet from the R package glmnet does automatic cross-validation on a grid of $\lambda$ values used for $\ell_1$-penalized regression problems. In particular, for the lasso. The glmnet | How to estimate shrinkage parameter in Lasso or ridge regression with >50K variables?
The function cv.glmnet from the R package glmnet does automatic cross-validation on a grid of $\lambda$ values used for $\ell_1$-penalized regression problems. In particular, for the lasso. The glmnet package also supports the more ge... | How to estimate shrinkage parameter in Lasso or ridge regression with >50K variables?
The function cv.glmnet from the R package glmnet does automatic cross-validation on a grid of $\lambda$ values used for $\ell_1$-penalized regression problems. In particular, for the lasso. The glmnet |
7,012 | How to estimate shrinkage parameter in Lasso or ridge regression with >50K variables? | This answer is MATLAB specific, however, the basic concepts should be quite similar to what you're used to with R...
In the case of MATLAB, you have the option to run lasso with cross validation enabled.
If you do so, the lasso function will report two critical parameter values
The lambda value that minimizes the cros... | How to estimate shrinkage parameter in Lasso or ridge regression with >50K variables? | This answer is MATLAB specific, however, the basic concepts should be quite similar to what you're used to with R...
In the case of MATLAB, you have the option to run lasso with cross validation enabl | How to estimate shrinkage parameter in Lasso or ridge regression with >50K variables?
This answer is MATLAB specific, however, the basic concepts should be quite similar to what you're used to with R...
In the case of MATLAB, you have the option to run lasso with cross validation enabled.
If you do so, the lasso functi... | How to estimate shrinkage parameter in Lasso or ridge regression with >50K variables?
This answer is MATLAB specific, however, the basic concepts should be quite similar to what you're used to with R...
In the case of MATLAB, you have the option to run lasso with cross validation enabl |
7,013 | How to estimate shrinkage parameter in Lasso or ridge regression with >50K variables? | I have had good success using effective AIC, that is using AIC with the effective degrees of freedom - see Gray JASA 87:942 1992 for effective d.f. This is implemented for $L_{2}$ penalty in the R rms package for linear and logistic models, and the rms pentrace function can be used to solve for the shrinkage coefficie... | How to estimate shrinkage parameter in Lasso or ridge regression with >50K variables? | I have had good success using effective AIC, that is using AIC with the effective degrees of freedom - see Gray JASA 87:942 1992 for effective d.f. This is implemented for $L_{2}$ penalty in the R rm | How to estimate shrinkage parameter in Lasso or ridge regression with >50K variables?
I have had good success using effective AIC, that is using AIC with the effective degrees of freedom - see Gray JASA 87:942 1992 for effective d.f. This is implemented for $L_{2}$ penalty in the R rms package for linear and logistic ... | How to estimate shrinkage parameter in Lasso or ridge regression with >50K variables?
I have had good success using effective AIC, that is using AIC with the effective degrees of freedom - see Gray JASA 87:942 1992 for effective d.f. This is implemented for $L_{2}$ penalty in the R rm |
7,014 | What are the differences between sparse coding and autoencoder? | Finding the differences can be done by looking at the models. Let's look at sparse coding first.
Sparse coding
Sparse coding minimizes the objective
$$
\mathcal{L}_{\text{sc}} = \underbrace{||WH - X||_2^2}_{\text{reconstruction term}} + \underbrace{\lambda ||H||_1}_{\text{sparsity term}}
$$
where $W$ is a matrix of b... | What are the differences between sparse coding and autoencoder? | Finding the differences can be done by looking at the models. Let's look at sparse coding first.
Sparse coding
Sparse coding minimizes the objective
$$
\mathcal{L}_{\text{sc}} = \underbrace{||WH - X| | What are the differences between sparse coding and autoencoder?
Finding the differences can be done by looking at the models. Let's look at sparse coding first.
Sparse coding
Sparse coding minimizes the objective
$$
\mathcal{L}_{\text{sc}} = \underbrace{||WH - X||_2^2}_{\text{reconstruction term}} + \underbrace{\lambd... | What are the differences between sparse coding and autoencoder?
Finding the differences can be done by looking at the models. Let's look at sparse coding first.
Sparse coding
Sparse coding minimizes the objective
$$
\mathcal{L}_{\text{sc}} = \underbrace{||WH - X| |
7,015 | What are the differences between sparse coding and autoencoder? | In neuroscience the term Neural Coding is used to refer to the patterns of electrical activity of neurons induced by a stimulus. Sparse Coding by its turn is one kind of pattern. A code is said to be sparse when a stimulus (like an image) provokes the activation of just a relatively small number of neurons, that combin... | What are the differences between sparse coding and autoencoder? | In neuroscience the term Neural Coding is used to refer to the patterns of electrical activity of neurons induced by a stimulus. Sparse Coding by its turn is one kind of pattern. A code is said to be | What are the differences between sparse coding and autoencoder?
In neuroscience the term Neural Coding is used to refer to the patterns of electrical activity of neurons induced by a stimulus. Sparse Coding by its turn is one kind of pattern. A code is said to be sparse when a stimulus (like an image) provokes the acti... | What are the differences between sparse coding and autoencoder?
In neuroscience the term Neural Coding is used to refer to the patterns of electrical activity of neurons induced by a stimulus. Sparse Coding by its turn is one kind of pattern. A code is said to be |
7,016 | What are the differences between sparse coding and autoencoder? | A sparse coder is kind of like half an auto-encoder. An auto-encoder works like:
input => neural net layer => hidden outputs => neural net layer => output
For back-propagation, the error signal, the loss, is: input - output
If we apply a sparsity constraint on the hidden outputs, then most will be zeros, and a fe... | What are the differences between sparse coding and autoencoder? | A sparse coder is kind of like half an auto-encoder. An auto-encoder works like:
input => neural net layer => hidden outputs => neural net layer => output
For back-propagation, the error signal, | What are the differences between sparse coding and autoencoder?
A sparse coder is kind of like half an auto-encoder. An auto-encoder works like:
input => neural net layer => hidden outputs => neural net layer => output
For back-propagation, the error signal, the loss, is: input - output
If we apply a sparsity con... | What are the differences between sparse coding and autoencoder?
A sparse coder is kind of like half an auto-encoder. An auto-encoder works like:
input => neural net layer => hidden outputs => neural net layer => output
For back-propagation, the error signal, |
7,017 | What are the differences between sparse coding and autoencoder? | You might want to read this recent paper, https://arxiv.org/abs/1708.03735v2 on precisely this same topic. In this paper the authors show that indeed one can set up an autoencoder such that the ground truth dictionary is a critical point of that autoencoder's squared loss function. | What are the differences between sparse coding and autoencoder? | You might want to read this recent paper, https://arxiv.org/abs/1708.03735v2 on precisely this same topic. In this paper the authors show that indeed one can set up an autoencoder such that the ground | What are the differences between sparse coding and autoencoder?
You might want to read this recent paper, https://arxiv.org/abs/1708.03735v2 on precisely this same topic. In this paper the authors show that indeed one can set up an autoencoder such that the ground truth dictionary is a critical point of that autoencode... | What are the differences between sparse coding and autoencoder?
You might want to read this recent paper, https://arxiv.org/abs/1708.03735v2 on precisely this same topic. In this paper the authors show that indeed one can set up an autoencoder such that the ground |
7,018 | Can SVM do stream learning one example at a time? | The streaming setting in machine learning is called "online learning". There is no exact support vector machine in the online setting (since the definition of the objective function is inherently for the batch setting). Probably the most straightforward generalization of the SVM to the online setting are passive-aggr... | Can SVM do stream learning one example at a time? | The streaming setting in machine learning is called "online learning". There is no exact support vector machine in the online setting (since the definition of the objective function is inherently for | Can SVM do stream learning one example at a time?
The streaming setting in machine learning is called "online learning". There is no exact support vector machine in the online setting (since the definition of the objective function is inherently for the batch setting). Probably the most straightforward generalization... | Can SVM do stream learning one example at a time?
The streaming setting in machine learning is called "online learning". There is no exact support vector machine in the online setting (since the definition of the objective function is inherently for |
7,019 | Can SVM do stream learning one example at a time? | I've always found the implicit updates framework (that includes the passive-aggressive algorithms mentioned in another answer here) to be unnecessarily more complex than the explicit updates framework (not to mention that implicit updates can be much slower than the explicit ones unless a closed-form solution for impli... | Can SVM do stream learning one example at a time? | I've always found the implicit updates framework (that includes the passive-aggressive algorithms mentioned in another answer here) to be unnecessarily more complex than the explicit updates framework | Can SVM do stream learning one example at a time?
I've always found the implicit updates framework (that includes the passive-aggressive algorithms mentioned in another answer here) to be unnecessarily more complex than the explicit updates framework (not to mention that implicit updates can be much slower than the exp... | Can SVM do stream learning one example at a time?
I've always found the implicit updates framework (that includes the passive-aggressive algorithms mentioned in another answer here) to be unnecessarily more complex than the explicit updates framework |
7,020 | Can SVM do stream learning one example at a time? | LASVM is one of the most popular online learning variants of the SVM.
Linear SVMs can also be trained using stochastic gradient descent, just like any linear model. | Can SVM do stream learning one example at a time? | LASVM is one of the most popular online learning variants of the SVM.
Linear SVMs can also be trained using stochastic gradient descent, just like any linear model. | Can SVM do stream learning one example at a time?
LASVM is one of the most popular online learning variants of the SVM.
Linear SVMs can also be trained using stochastic gradient descent, just like any linear model. | Can SVM do stream learning one example at a time?
LASVM is one of the most popular online learning variants of the SVM.
Linear SVMs can also be trained using stochastic gradient descent, just like any linear model. |
7,021 | Can SVM do stream learning one example at a time? | Please refer to paper SVM Incremental Learning, Adaptation, and Optimization, which proposed an online SVM for binary classification.
The code of above paper can be found here. In the code, two ways of online training are introduced:
train the SVM incrementally on one example at a time by calling svmtrain(), and
per... | Can SVM do stream learning one example at a time? | Please refer to paper SVM Incremental Learning, Adaptation, and Optimization, which proposed an online SVM for binary classification.
The code of above paper can be found here. In the code, two ways o | Can SVM do stream learning one example at a time?
Please refer to paper SVM Incremental Learning, Adaptation, and Optimization, which proposed an online SVM for binary classification.
The code of above paper can be found here. In the code, two ways of online training are introduced:
train the SVM incrementally on one... | Can SVM do stream learning one example at a time?
Please refer to paper SVM Incremental Learning, Adaptation, and Optimization, which proposed an online SVM for binary classification.
The code of above paper can be found here. In the code, two ways o |
7,022 | Can SVM do stream learning one example at a time? | Online Learning with Kernels discusses online learning in general kernel settings.
Excerpt from the abstract -
"Kernel based algorithms such as support vector machines have achieved considerable success in various problems in the batch setting where all of the training data is available in advance. Support vector mac... | Can SVM do stream learning one example at a time? | Online Learning with Kernels discusses online learning in general kernel settings.
Excerpt from the abstract -
"Kernel based algorithms such as support vector machines have achieved considerable suc | Can SVM do stream learning one example at a time?
Online Learning with Kernels discusses online learning in general kernel settings.
Excerpt from the abstract -
"Kernel based algorithms such as support vector machines have achieved considerable success in various problems in the batch setting where all of the trainin... | Can SVM do stream learning one example at a time?
Online Learning with Kernels discusses online learning in general kernel settings.
Excerpt from the abstract -
"Kernel based algorithms such as support vector machines have achieved considerable suc |
7,023 | What is the "capacity" of a machine learning model? | Capacity is an informal term. It's very close (if not a synonym) for model complexity. It's a way to talk about how complicated a pattern or relationship a model can express. You could expect a model with higher capacity to be able to model more relationships between more variables than a model with a lower capacity.
D... | What is the "capacity" of a machine learning model? | Capacity is an informal term. It's very close (if not a synonym) for model complexity. It's a way to talk about how complicated a pattern or relationship a model can express. You could expect a model | What is the "capacity" of a machine learning model?
Capacity is an informal term. It's very close (if not a synonym) for model complexity. It's a way to talk about how complicated a pattern or relationship a model can express. You could expect a model with higher capacity to be able to model more relationships between ... | What is the "capacity" of a machine learning model?
Capacity is an informal term. It's very close (if not a synonym) for model complexity. It's a way to talk about how complicated a pattern or relationship a model can express. You could expect a model |
7,024 | What is the "capacity" of a machine learning model? | In the fundamental challenge of Machine Learning: Does the model I built truly generalize? one way to approach it is by using model capacity.
Higher the model capacity, the more expressive the model (i.e., it can accommodate more variation).
Capacity needs to be tuned with respect to the amount of data at hand. If a da... | What is the "capacity" of a machine learning model? | In the fundamental challenge of Machine Learning: Does the model I built truly generalize? one way to approach it is by using model capacity.
Higher the model capacity, the more expressive the model ( | What is the "capacity" of a machine learning model?
In the fundamental challenge of Machine Learning: Does the model I built truly generalize? one way to approach it is by using model capacity.
Higher the model capacity, the more expressive the model (i.e., it can accommodate more variation).
Capacity needs to be tuned... | What is the "capacity" of a machine learning model?
In the fundamental challenge of Machine Learning: Does the model I built truly generalize? one way to approach it is by using model capacity.
Higher the model capacity, the more expressive the model ( |
7,025 | When is it appropriate to use an improper scoring rule? | It is appropriate to use an improper scoring rule when the purpose is actually forecasting, but not inference. I don't really care whether another forecaster is cheating or not when I am the one who is going to be doing the forecast.
Proper scoring rules ensure that during estimation process the model approaches the tr... | When is it appropriate to use an improper scoring rule? | It is appropriate to use an improper scoring rule when the purpose is actually forecasting, but not inference. I don't really care whether another forecaster is cheating or not when I am the one who i | When is it appropriate to use an improper scoring rule?
It is appropriate to use an improper scoring rule when the purpose is actually forecasting, but not inference. I don't really care whether another forecaster is cheating or not when I am the one who is going to be doing the forecast.
Proper scoring rules ensure th... | When is it appropriate to use an improper scoring rule?
It is appropriate to use an improper scoring rule when the purpose is actually forecasting, but not inference. I don't really care whether another forecaster is cheating or not when I am the one who i |
7,026 | When is it appropriate to use an improper scoring rule? | Accuracy (i.e., percent correctly classified) is an improper scoring rule, so in some sense people do it all the time.
More generally, any scoring rule that forces predictions into a pre-defined category is going to be improper. Classification is an extreme case of this (the only allowable forecasts are 0% and 100%), b... | When is it appropriate to use an improper scoring rule? | Accuracy (i.e., percent correctly classified) is an improper scoring rule, so in some sense people do it all the time.
More generally, any scoring rule that forces predictions into a pre-defined categ | When is it appropriate to use an improper scoring rule?
Accuracy (i.e., percent correctly classified) is an improper scoring rule, so in some sense people do it all the time.
More generally, any scoring rule that forces predictions into a pre-defined category is going to be improper. Classification is an extreme case o... | When is it appropriate to use an improper scoring rule?
Accuracy (i.e., percent correctly classified) is an improper scoring rule, so in some sense people do it all the time.
More generally, any scoring rule that forces predictions into a pre-defined categ |
7,027 | When is it appropriate to use an improper scoring rule? | A simplified answer, as indicated by Cagdas Ozgenc, might be: whenever you do not aim for the true predictive distribution.
A second aspect is the difference between fitting/estimation, inference, and forecast comparison. When you fit by minimizing a proper scoring rule and then add a penalty to deal with overfitting, ... | When is it appropriate to use an improper scoring rule? | A simplified answer, as indicated by Cagdas Ozgenc, might be: whenever you do not aim for the true predictive distribution.
A second aspect is the difference between fitting/estimation, inference, and | When is it appropriate to use an improper scoring rule?
A simplified answer, as indicated by Cagdas Ozgenc, might be: whenever you do not aim for the true predictive distribution.
A second aspect is the difference between fitting/estimation, inference, and forecast comparison. When you fit by minimizing a proper scorin... | When is it appropriate to use an improper scoring rule?
A simplified answer, as indicated by Cagdas Ozgenc, might be: whenever you do not aim for the true predictive distribution.
A second aspect is the difference between fitting/estimation, inference, and |
7,028 | What percentage of a population needs a test in order to estimate prevalence of a disease? Say, COVID-19 | 1) Making some assumptions about the population size (namely that it is large enough that a binomial model is appropriate), the prevalence of a disease in a population at a particular time can be obtained by sampling simple random sampling of people and finding who is sick. That is a binomial random variable and the ... | What percentage of a population needs a test in order to estimate prevalence of a disease? Say, COVI | 1) Making some assumptions about the population size (namely that it is large enough that a binomial model is appropriate), the prevalence of a disease in a population at a particular time can be obt | What percentage of a population needs a test in order to estimate prevalence of a disease? Say, COVID-19
1) Making some assumptions about the population size (namely that it is large enough that a binomial model is appropriate), the prevalence of a disease in a population at a particular time can be obtained by sampli... | What percentage of a population needs a test in order to estimate prevalence of a disease? Say, COVI
1) Making some assumptions about the population size (namely that it is large enough that a binomial model is appropriate), the prevalence of a disease in a population at a particular time can be obt |
7,029 | What percentage of a population needs a test in order to estimate prevalence of a disease? Say, COVID-19 | It has been answered by Dimitri Pananos, I will only add that in order to estimate the prevalence with pre-set precision you need an absolute sample size which is pretty much invariant with the population size (only when the sample is a substantial part of the target population you have a non-negligible finite populati... | What percentage of a population needs a test in order to estimate prevalence of a disease? Say, COVI | It has been answered by Dimitri Pananos, I will only add that in order to estimate the prevalence with pre-set precision you need an absolute sample size which is pretty much invariant with the popula | What percentage of a population needs a test in order to estimate prevalence of a disease? Say, COVID-19
It has been answered by Dimitri Pananos, I will only add that in order to estimate the prevalence with pre-set precision you need an absolute sample size which is pretty much invariant with the population size (only... | What percentage of a population needs a test in order to estimate prevalence of a disease? Say, COVI
It has been answered by Dimitri Pananos, I will only add that in order to estimate the prevalence with pre-set precision you need an absolute sample size which is pretty much invariant with the popula |
7,030 | What percentage of a population needs a test in order to estimate prevalence of a disease? Say, COVID-19 | I'll go in a somewhat different direction and say that it depends...
Of course any sampling is based on the notion that the sampling is truly random. Trying to account for non-randomness in the sample tremendously complicates the situation.
This type of yes/no measurement is non-parametric. Such tests need a larger s... | What percentage of a population needs a test in order to estimate prevalence of a disease? Say, COVI | I'll go in a somewhat different direction and say that it depends...
Of course any sampling is based on the notion that the sampling is truly random. Trying to account for non-randomness in the sampl | What percentage of a population needs a test in order to estimate prevalence of a disease? Say, COVID-19
I'll go in a somewhat different direction and say that it depends...
Of course any sampling is based on the notion that the sampling is truly random. Trying to account for non-randomness in the sample tremendously ... | What percentage of a population needs a test in order to estimate prevalence of a disease? Say, COVI
I'll go in a somewhat different direction and say that it depends...
Of course any sampling is based on the notion that the sampling is truly random. Trying to account for non-randomness in the sampl |
7,031 | Error metrics for cross-validating Poisson models | There are a couple of proper and strictly proper scoring rules for count data you can use. Scoring rules are penalties $s(y,P)$ introduced with $P$ being the predictive distribution and $y$ the observed value. They have a number of desirable properties, first and foremost that a forecast that is closer to the true prob... | Error metrics for cross-validating Poisson models | There are a couple of proper and strictly proper scoring rules for count data you can use. Scoring rules are penalties $s(y,P)$ introduced with $P$ being the predictive distribution and $y$ the observ | Error metrics for cross-validating Poisson models
There are a couple of proper and strictly proper scoring rules for count data you can use. Scoring rules are penalties $s(y,P)$ introduced with $P$ being the predictive distribution and $y$ the observed value. They have a number of desirable properties, first and foremo... | Error metrics for cross-validating Poisson models
There are a couple of proper and strictly proper scoring rules for count data you can use. Scoring rules are penalties $s(y,P)$ introduced with $P$ being the predictive distribution and $y$ the observ |
7,032 | What is the connection between credible regions and Bayesian hypothesis tests? | I managed to come up with an example where a connection exists. It seems to depend heavily on my choice of loss function and the use of composite hypotheses though.
I start with a general example, which is then followed by a simple special case involving the normal distribution.
General example
For an unknown parameter... | What is the connection between credible regions and Bayesian hypothesis tests? | I managed to come up with an example where a connection exists. It seems to depend heavily on my choice of loss function and the use of composite hypotheses though.
I start with a general example, whi | What is the connection between credible regions and Bayesian hypothesis tests?
I managed to come up with an example where a connection exists. It seems to depend heavily on my choice of loss function and the use of composite hypotheses though.
I start with a general example, which is then followed by a simple special c... | What is the connection between credible regions and Bayesian hypothesis tests?
I managed to come up with an example where a connection exists. It seems to depend heavily on my choice of loss function and the use of composite hypotheses though.
I start with a general example, whi |
7,033 | What is the connection between credible regions and Bayesian hypothesis tests? | Michael and Fraijo suggested that simply checking whether the parameter value of interested was contained in some credible region was the Bayesian equivalent of inverting confidence intervals. I was a bit skeptical about this at first, since it wasn't obvious to me that this procedure really resulted in a Bayesian test... | What is the connection between credible regions and Bayesian hypothesis tests? | Michael and Fraijo suggested that simply checking whether the parameter value of interested was contained in some credible region was the Bayesian equivalent of inverting confidence intervals. I was a | What is the connection between credible regions and Bayesian hypothesis tests?
Michael and Fraijo suggested that simply checking whether the parameter value of interested was contained in some credible region was the Bayesian equivalent of inverting confidence intervals. I was a bit skeptical about this at first, since... | What is the connection between credible regions and Bayesian hypothesis tests?
Michael and Fraijo suggested that simply checking whether the parameter value of interested was contained in some credible region was the Bayesian equivalent of inverting confidence intervals. I was a |
7,034 | What is the connection between credible regions and Bayesian hypothesis tests? | I coincidentally read your arXiv paper prior to coming to this question and already wrote a blog entry on it (scheduled to appear on October, 08). To sum up, I find your construction of theoretical interest, but also think it is too contrived to be recommended, esp. as it does not seem to solve the point-null hypothesi... | What is the connection between credible regions and Bayesian hypothesis tests? | I coincidentally read your arXiv paper prior to coming to this question and already wrote a blog entry on it (scheduled to appear on October, 08). To sum up, I find your construction of theoretical in | What is the connection between credible regions and Bayesian hypothesis tests?
I coincidentally read your arXiv paper prior to coming to this question and already wrote a blog entry on it (scheduled to appear on October, 08). To sum up, I find your construction of theoretical interest, but also think it is too contrive... | What is the connection between credible regions and Bayesian hypothesis tests?
I coincidentally read your arXiv paper prior to coming to this question and already wrote a blog entry on it (scheduled to appear on October, 08). To sum up, I find your construction of theoretical in |
7,035 | What is the connection between credible regions and Bayesian hypothesis tests? | You can use a credible interval (or HPD region) for Bayesian hypothesis testing. I don't think it is common; though, to be fair I do not see much nor do I use formal Bayesian Hypothesis testing in practice. Bayes factors are occasionally used (and in Robert's "Bayesian Core" somewhat lauded) in hypothesis testing set... | What is the connection between credible regions and Bayesian hypothesis tests? | You can use a credible interval (or HPD region) for Bayesian hypothesis testing. I don't think it is common; though, to be fair I do not see much nor do I use formal Bayesian Hypothesis testing in pr | What is the connection between credible regions and Bayesian hypothesis tests?
You can use a credible interval (or HPD region) for Bayesian hypothesis testing. I don't think it is common; though, to be fair I do not see much nor do I use formal Bayesian Hypothesis testing in practice. Bayes factors are occasionally u... | What is the connection between credible regions and Bayesian hypothesis tests?
You can use a credible interval (or HPD region) for Bayesian hypothesis testing. I don't think it is common; though, to be fair I do not see much nor do I use formal Bayesian Hypothesis testing in pr |
7,036 | What is the connection between credible regions and Bayesian hypothesis tests? | A credible region is just a region where the integral of the posterior density over the region is a specified probability e.g. 0.95. One way to form a Bayesian hypothesis test is to see whether or not the null hypothesized value(s) of the parameter(s) fall in the credible region. In this way we can have a similar 1-1 ... | What is the connection between credible regions and Bayesian hypothesis tests? | A credible region is just a region where the integral of the posterior density over the region is a specified probability e.g. 0.95. One way to form a Bayesian hypothesis test is to see whether or not | What is the connection between credible regions and Bayesian hypothesis tests?
A credible region is just a region where the integral of the posterior density over the region is a specified probability e.g. 0.95. One way to form a Bayesian hypothesis test is to see whether or not the null hypothesized value(s) of the pa... | What is the connection between credible regions and Bayesian hypothesis tests?
A credible region is just a region where the integral of the posterior density over the region is a specified probability e.g. 0.95. One way to form a Bayesian hypothesis test is to see whether or not |
7,037 | What is the connection between credible regions and Bayesian hypothesis tests? | Let me give it how I got it reading Tim's answer.
It is based on the table views with hypothesis (estimated parameter) in columns and observations in the rows.
In the first table, you have col probabilities sum to 1, i.e. they are conditional probabilities, whose condition, getting into the column event is supplied i... | What is the connection between credible regions and Bayesian hypothesis tests? | Let me give it how I got it reading Tim's answer.
It is based on the table views with hypothesis (estimated parameter) in columns and observations in the rows.
In the first table, you have col proba | What is the connection between credible regions and Bayesian hypothesis tests?
Let me give it how I got it reading Tim's answer.
It is based on the table views with hypothesis (estimated parameter) in columns and observations in the rows.
In the first table, you have col probabilities sum to 1, i.e. they are conditio... | What is the connection between credible regions and Bayesian hypothesis tests?
Let me give it how I got it reading Tim's answer.
It is based on the table views with hypothesis (estimated parameter) in columns and observations in the rows.
In the first table, you have col proba |
7,038 | What's the relation between hierarchical models, neural networks, graphical models, bayesian networks? | A Bayesian network is a type of graphical model. The other "big" type of graphical model is a Markov Random Field (MRF). Graphical models are used for inference, estimation and in general, to model the world.
The term hierarchical model is used to mean many things in different areas.
While neural networks come with "g... | What's the relation between hierarchical models, neural networks, graphical models, bayesian network | A Bayesian network is a type of graphical model. The other "big" type of graphical model is a Markov Random Field (MRF). Graphical models are used for inference, estimation and in general, to model t | What's the relation between hierarchical models, neural networks, graphical models, bayesian networks?
A Bayesian network is a type of graphical model. The other "big" type of graphical model is a Markov Random Field (MRF). Graphical models are used for inference, estimation and in general, to model the world.
The ter... | What's the relation between hierarchical models, neural networks, graphical models, bayesian network
A Bayesian network is a type of graphical model. The other "big" type of graphical model is a Markov Random Field (MRF). Graphical models are used for inference, estimation and in general, to model t |
7,039 | What's the relation between hierarchical models, neural networks, graphical models, bayesian networks? | As @carlosdc said, a bayesian network is a type of Graphical Model (i.e., a directed acyclic graph (DAG) whose structure defines a set of conditional independence properties). Hierarchical Bayes Models can also be represented as DAGs; Hierarchical Naive Bayes Classifiers for uncertain data, by Bellazzi et al., provides... | What's the relation between hierarchical models, neural networks, graphical models, bayesian network | As @carlosdc said, a bayesian network is a type of Graphical Model (i.e., a directed acyclic graph (DAG) whose structure defines a set of conditional independence properties). Hierarchical Bayes Model | What's the relation between hierarchical models, neural networks, graphical models, bayesian networks?
As @carlosdc said, a bayesian network is a type of Graphical Model (i.e., a directed acyclic graph (DAG) whose structure defines a set of conditional independence properties). Hierarchical Bayes Models can also be rep... | What's the relation between hierarchical models, neural networks, graphical models, bayesian network
As @carlosdc said, a bayesian network is a type of Graphical Model (i.e., a directed acyclic graph (DAG) whose structure defines a set of conditional independence properties). Hierarchical Bayes Model |
7,040 | What's the relation between hierarchical models, neural networks, graphical models, bayesian networks? | Neural networks does not require priors, but each hidden node (neurons) of a neural network can be considered as CPD
- Noisy OR/AND CPD for a linear node
- Sigmoid CPD for a logistic node
So, neural networks could be viewed as multiple layers of hidden nodes, each with linear/sigmoidal CPDs
Koller's class on Coursera O... | What's the relation between hierarchical models, neural networks, graphical models, bayesian network | Neural networks does not require priors, but each hidden node (neurons) of a neural network can be considered as CPD
- Noisy OR/AND CPD for a linear node
- Sigmoid CPD for a logistic node
So, neural n | What's the relation between hierarchical models, neural networks, graphical models, bayesian networks?
Neural networks does not require priors, but each hidden node (neurons) of a neural network can be considered as CPD
- Noisy OR/AND CPD for a linear node
- Sigmoid CPD for a logistic node
So, neural networks could be ... | What's the relation between hierarchical models, neural networks, graphical models, bayesian network
Neural networks does not require priors, but each hidden node (neurons) of a neural network can be considered as CPD
- Noisy OR/AND CPD for a linear node
- Sigmoid CPD for a logistic node
So, neural n |
7,041 | Is it possible to calculate AIC and BIC for lasso regression models? | You may also find the following papers to be of interest:
R. J. Tibshirani and J. Taylor (2011), Degrees of freedom in lasso problems, arXiv preprint:1111.0653.
H. Zou, T. Hastie and R. Tibshirani (2007), On the degrees of freedom of the lasso, Annals of Statistics 35 (5), 2173–2192. | Is it possible to calculate AIC and BIC for lasso regression models? | You may also find the following papers to be of interest:
R. J. Tibshirani and J. Taylor (2011), Degrees of freedom in lasso problems, arXiv preprint:1111.0653.
H. Zou, T. Hastie and R. Tibshirani ( | Is it possible to calculate AIC and BIC for lasso regression models?
You may also find the following papers to be of interest:
R. J. Tibshirani and J. Taylor (2011), Degrees of freedom in lasso problems, arXiv preprint:1111.0653.
H. Zou, T. Hastie and R. Tibshirani (2007), On the degrees of freedom of the lasso, Anna... | Is it possible to calculate AIC and BIC for lasso regression models?
You may also find the following papers to be of interest:
R. J. Tibshirani and J. Taylor (2011), Degrees of freedom in lasso problems, arXiv preprint:1111.0653.
H. Zou, T. Hastie and R. Tibshirani ( |
7,042 | Is it possible to calculate AIC and BIC for lasso regression models? | I was struggling a lot with a way how to calculate AIC and BIC for glmnet models. However, after quite a lot of searching, I found on the third page of google results the answer. It can be found here. I am posting it here for future readers as I believe I cannot be the only one.
In the end, I implemented the AIC and BI... | Is it possible to calculate AIC and BIC for lasso regression models? | I was struggling a lot with a way how to calculate AIC and BIC for glmnet models. However, after quite a lot of searching, I found on the third page of google results the answer. It can be found here. | Is it possible to calculate AIC and BIC for lasso regression models?
I was struggling a lot with a way how to calculate AIC and BIC for glmnet models. However, after quite a lot of searching, I found on the third page of google results the answer. It can be found here. I am posting it here for future readers as I belie... | Is it possible to calculate AIC and BIC for lasso regression models?
I was struggling a lot with a way how to calculate AIC and BIC for glmnet models. However, after quite a lot of searching, I found on the third page of google results the answer. It can be found here. |
7,043 | Is it possible to calculate AIC and BIC for lasso regression models? | In the link referenced by johnnyheineken, the author states:
I'm afraid that the two quantities available from the glmnet object(dev.ratio, nulldev) are not enough to obtain the likelihood for the model, which you need to compute AICc. You have two equations in three unknowns: likelihood(null), likelihood(model), and ... | Is it possible to calculate AIC and BIC for lasso regression models? | In the link referenced by johnnyheineken, the author states:
I'm afraid that the two quantities available from the glmnet object(dev.ratio, nulldev) are not enough to obtain the likelihood for the mo | Is it possible to calculate AIC and BIC for lasso regression models?
In the link referenced by johnnyheineken, the author states:
I'm afraid that the two quantities available from the glmnet object(dev.ratio, nulldev) are not enough to obtain the likelihood for the model, which you need to compute AICc. You have two e... | Is it possible to calculate AIC and BIC for lasso regression models?
In the link referenced by johnnyheineken, the author states:
I'm afraid that the two quantities available from the glmnet object(dev.ratio, nulldev) are not enough to obtain the likelihood for the mo |
7,044 | Do we need a test set when using k-fold cross-validation? | In the K-Fold method, do we still hold out a test set for the very end, and only use the remaining data for training and hyperparameter tuning (ie. we split the remaining data into k folds, and then use the average accuracy after training with each fold (or whatever performance metric we choose) to tune our hyperparame... | Do we need a test set when using k-fold cross-validation? | In the K-Fold method, do we still hold out a test set for the very end, and only use the remaining data for training and hyperparameter tuning (ie. we split the remaining data into k folds, and then u | Do we need a test set when using k-fold cross-validation?
In the K-Fold method, do we still hold out a test set for the very end, and only use the remaining data for training and hyperparameter tuning (ie. we split the remaining data into k folds, and then use the average accuracy after training with each fold (or what... | Do we need a test set when using k-fold cross-validation?
In the K-Fold method, do we still hold out a test set for the very end, and only use the remaining data for training and hyperparameter tuning (ie. we split the remaining data into k folds, and then u |
7,045 | Do we need a test set when using k-fold cross-validation? | Generally, yes.
Basically you we are talking about the bias-variance tradeoff. If you use data to build up your model (training and validation data) and you iterate over different hyperparameters and you try to maximize an averaged performence metric your model might not be as good as indicated.
However, especially in... | Do we need a test set when using k-fold cross-validation? | Generally, yes.
Basically you we are talking about the bias-variance tradeoff. If you use data to build up your model (training and validation data) and you iterate over different hyperparameters and | Do we need a test set when using k-fold cross-validation?
Generally, yes.
Basically you we are talking about the bias-variance tradeoff. If you use data to build up your model (training and validation data) and you iterate over different hyperparameters and you try to maximize an averaged performence metric your model ... | Do we need a test set when using k-fold cross-validation?
Generally, yes.
Basically you we are talking about the bias-variance tradeoff. If you use data to build up your model (training and validation data) and you iterate over different hyperparameters and |
7,046 | Do we need a test set when using k-fold cross-validation? | Ideally, validation (for model selection) and final test should not be mixed. However, if your k value is high, or it is leave-one-out, using test result to guide your model selection is less harmful. In this scenario, if you are writing an academic paper, do not do it (unless you bother to explain)-- meaning always ha... | Do we need a test set when using k-fold cross-validation? | Ideally, validation (for model selection) and final test should not be mixed. However, if your k value is high, or it is leave-one-out, using test result to guide your model selection is less harmful. | Do we need a test set when using k-fold cross-validation?
Ideally, validation (for model selection) and final test should not be mixed. However, if your k value is high, or it is leave-one-out, using test result to guide your model selection is less harmful. In this scenario, if you are writing an academic paper, do no... | Do we need a test set when using k-fold cross-validation?
Ideally, validation (for model selection) and final test should not be mixed. However, if your k value is high, or it is leave-one-out, using test result to guide your model selection is less harmful. |
7,047 | Clustering a dataset with both discrete and continuous variables | So you've been told you need an appropriate distance measure. Here are some leads:
Clustering mixed data
A generalized Mahalanobis distance for mixed data
Estimating the Mahalanobis distance from mixed continuous and discrete data
Generalization of the Mahalanobis distance in the mixed case
Distance functions for cate... | Clustering a dataset with both discrete and continuous variables | So you've been told you need an appropriate distance measure. Here are some leads:
Clustering mixed data
A generalized Mahalanobis distance for mixed data
Estimating the Mahalanobis distance from mix | Clustering a dataset with both discrete and continuous variables
So you've been told you need an appropriate distance measure. Here are some leads:
Clustering mixed data
A generalized Mahalanobis distance for mixed data
Estimating the Mahalanobis distance from mixed continuous and discrete data
Generalization of the M... | Clustering a dataset with both discrete and continuous variables
So you've been told you need an appropriate distance measure. Here are some leads:
Clustering mixed data
A generalized Mahalanobis distance for mixed data
Estimating the Mahalanobis distance from mix |
7,048 | Clustering a dataset with both discrete and continuous variables | I've had to deal with this kind of problem in the past, and I think there could be 2 interesting approaches:
Continuousification: transform symbolic attributes with a sequence of integers. There are several ways to do this, all of which described in this paper. You can try NBF, VDM and MDV algorithms.
Discretization: ... | Clustering a dataset with both discrete and continuous variables | I've had to deal with this kind of problem in the past, and I think there could be 2 interesting approaches:
Continuousification: transform symbolic attributes with a sequence of integers. There are | Clustering a dataset with both discrete and continuous variables
I've had to deal with this kind of problem in the past, and I think there could be 2 interesting approaches:
Continuousification: transform symbolic attributes with a sequence of integers. There are several ways to do this, all of which described in this... | Clustering a dataset with both discrete and continuous variables
I've had to deal with this kind of problem in the past, and I think there could be 2 interesting approaches:
Continuousification: transform symbolic attributes with a sequence of integers. There are |
7,049 | Clustering a dataset with both discrete and continuous variables | K-means obviously doesn't make any sense, as it computes means (which are nonsensical). Same goes for GMM.
You might want to try distance-based clustering algorithms with appropriate distance functions, for example DBSCAN.
The main challenge is to find a distance function!
While you could put a different distance funct... | Clustering a dataset with both discrete and continuous variables | K-means obviously doesn't make any sense, as it computes means (which are nonsensical). Same goes for GMM.
You might want to try distance-based clustering algorithms with appropriate distance function | Clustering a dataset with both discrete and continuous variables
K-means obviously doesn't make any sense, as it computes means (which are nonsensical). Same goes for GMM.
You might want to try distance-based clustering algorithms with appropriate distance functions, for example DBSCAN.
The main challenge is to find a ... | Clustering a dataset with both discrete and continuous variables
K-means obviously doesn't make any sense, as it computes means (which are nonsensical). Same goes for GMM.
You might want to try distance-based clustering algorithms with appropriate distance function |
7,050 | Clustering a dataset with both discrete and continuous variables | If you are comfortable working with a distance matrix of size num_of_samples x num_of_samples, you could use random forests, as well.
Click here for a reference paper titled Unsupervised learning with random forest predictors.
The idea is creating a synthetic dataset by shuffling values in the original dataset and trai... | Clustering a dataset with both discrete and continuous variables | If you are comfortable working with a distance matrix of size num_of_samples x num_of_samples, you could use random forests, as well.
Click here for a reference paper titled Unsupervised learning with | Clustering a dataset with both discrete and continuous variables
If you are comfortable working with a distance matrix of size num_of_samples x num_of_samples, you could use random forests, as well.
Click here for a reference paper titled Unsupervised learning with random forest predictors.
The idea is creating a synth... | Clustering a dataset with both discrete and continuous variables
If you are comfortable working with a distance matrix of size num_of_samples x num_of_samples, you could use random forests, as well.
Click here for a reference paper titled Unsupervised learning with |
7,051 | Clustering a dataset with both discrete and continuous variables | Mixed approach to be adopted:
1) Use classification technique (C4.5 decision tree) to classify the data set into 2 classes.
2) Once it is done, leave categorical variables and proceed with continuous variables for clustering. | Clustering a dataset with both discrete and continuous variables | Mixed approach to be adopted:
1) Use classification technique (C4.5 decision tree) to classify the data set into 2 classes.
2) Once it is done, leave categorical variables and proceed with continuous | Clustering a dataset with both discrete and continuous variables
Mixed approach to be adopted:
1) Use classification technique (C4.5 decision tree) to classify the data set into 2 classes.
2) Once it is done, leave categorical variables and proceed with continuous variables for clustering. | Clustering a dataset with both discrete and continuous variables
Mixed approach to be adopted:
1) Use classification technique (C4.5 decision tree) to classify the data set into 2 classes.
2) Once it is done, leave categorical variables and proceed with continuous |
7,052 | Sampling for Imbalanced Data in Regression | Imbalance is not necessarily a problem, but how you get there can be. It is unsound to base your sampling strategy on the target variable. Because this variable incorporates the randomness in your regression model, if you sample based on this you will have big problems doing any kind of inference. I doubt it is poss... | Sampling for Imbalanced Data in Regression | Imbalance is not necessarily a problem, but how you get there can be. It is unsound to base your sampling strategy on the target variable. Because this variable incorporates the randomness in your r | Sampling for Imbalanced Data in Regression
Imbalance is not necessarily a problem, but how you get there can be. It is unsound to base your sampling strategy on the target variable. Because this variable incorporates the randomness in your regression model, if you sample based on this you will have big problems doing... | Sampling for Imbalanced Data in Regression
Imbalance is not necessarily a problem, but how you get there can be. It is unsound to base your sampling strategy on the target variable. Because this variable incorporates the randomness in your r |
7,053 | Sampling for Imbalanced Data in Regression | Updated answer, May 2020:
There is actually a field of research that deals particularly with this question and has developed practically feasible solutions. It is called Covariate Shift Adaptation and has been popularized by a series of highly cited papers by Sugiyama et al., starting around 2007 (I believe). There is ... | Sampling for Imbalanced Data in Regression | Updated answer, May 2020:
There is actually a field of research that deals particularly with this question and has developed practically feasible solutions. It is called Covariate Shift Adaptation and | Sampling for Imbalanced Data in Regression
Updated answer, May 2020:
There is actually a field of research that deals particularly with this question and has developed practically feasible solutions. It is called Covariate Shift Adaptation and has been popularized by a series of highly cited papers by Sugiyama et al., ... | Sampling for Imbalanced Data in Regression
Updated answer, May 2020:
There is actually a field of research that deals particularly with this question and has developed practically feasible solutions. It is called Covariate Shift Adaptation and |
7,054 | Sampling for Imbalanced Data in Regression | It is a question of whether you are doing causal analysis or prediction.
Resampling on the target variable for training for the purposes of prediction works as long as one tests on an non-resampled hold out sample. The final performance chart must be based solely on the hold out. For most accuracy in the determination... | Sampling for Imbalanced Data in Regression | It is a question of whether you are doing causal analysis or prediction.
Resampling on the target variable for training for the purposes of prediction works as long as one tests on an non-resampled ho | Sampling for Imbalanced Data in Regression
It is a question of whether you are doing causal analysis or prediction.
Resampling on the target variable for training for the purposes of prediction works as long as one tests on an non-resampled hold out sample. The final performance chart must be based solely on the hold o... | Sampling for Imbalanced Data in Regression
It is a question of whether you are doing causal analysis or prediction.
Resampling on the target variable for training for the purposes of prediction works as long as one tests on an non-resampled ho |
7,055 | Sampling for Imbalanced Data in Regression | first of all, 1:10 ration is not bad at all. there are simple way of undoing sampling-
1) for classification problem, If you have sub-sampled any negative class by 10. the resulting probability is 10 times more what is should be. you can simple divide resulting probability by 10.(known as model re calibration)
2) Face... | Sampling for Imbalanced Data in Regression | first of all, 1:10 ration is not bad at all. there are simple way of undoing sampling-
1) for classification problem, If you have sub-sampled any negative class by 10. the resulting probability is 10 | Sampling for Imbalanced Data in Regression
first of all, 1:10 ration is not bad at all. there are simple way of undoing sampling-
1) for classification problem, If you have sub-sampled any negative class by 10. the resulting probability is 10 times more what is should be. you can simple divide resulting probability by... | Sampling for Imbalanced Data in Regression
first of all, 1:10 ration is not bad at all. there are simple way of undoing sampling-
1) for classification problem, If you have sub-sampled any negative class by 10. the resulting probability is 10 |
7,056 | Sampling for Imbalanced Data in Regression | A recent technique proposed for dealing with imbalanced distribution in regression is the Weighted Relevance-based Combination Strategy (WERCS). Branco et al. (2019) https://doi.org/10.1016/j.neucom.2018.11.100.
The author has reproducible R implementation of the method at GitHub here: https://github.com/paobranco/Pre... | Sampling for Imbalanced Data in Regression | A recent technique proposed for dealing with imbalanced distribution in regression is the Weighted Relevance-based Combination Strategy (WERCS). Branco et al. (2019) https://doi.org/10.1016/j.neucom.2 | Sampling for Imbalanced Data in Regression
A recent technique proposed for dealing with imbalanced distribution in regression is the Weighted Relevance-based Combination Strategy (WERCS). Branco et al. (2019) https://doi.org/10.1016/j.neucom.2018.11.100.
The author has reproducible R implementation of the method at Gi... | Sampling for Imbalanced Data in Regression
A recent technique proposed for dealing with imbalanced distribution in regression is the Weighted Relevance-based Combination Strategy (WERCS). Branco et al. (2019) https://doi.org/10.1016/j.neucom.2 |
7,057 | Mode, Class and Type of R objects | The class() is used to define/identify what "type" an object is from the point of view of object-oriented programming in R. So for
> x <- 1:3
> class(x)
[1] "integer"
any generic function that has an "integer" method will be used.
typeof() gives the "type" of object from R's point of view, whilst mode() gives the "typ... | Mode, Class and Type of R objects | The class() is used to define/identify what "type" an object is from the point of view of object-oriented programming in R. So for
> x <- 1:3
> class(x)
[1] "integer"
any generic function that has an | Mode, Class and Type of R objects
The class() is used to define/identify what "type" an object is from the point of view of object-oriented programming in R. So for
> x <- 1:3
> class(x)
[1] "integer"
any generic function that has an "integer" method will be used.
typeof() gives the "type" of object from R's point of ... | Mode, Class and Type of R objects
The class() is used to define/identify what "type" an object is from the point of view of object-oriented programming in R. So for
> x <- 1:3
> class(x)
[1] "integer"
any generic function that has an |
7,058 | Mode, Class and Type of R objects | From: https://www.mail-archive.com/r-help@r-project.org/msg17169.html :
'mode' is a mutually exclusive classification of objects according to
their basic structure. The 'atomic' modes are numeric, complex,
character and logical. Recursive objects have modes such as 'list' or
'function' or a few others. An object has... | Mode, Class and Type of R objects | From: https://www.mail-archive.com/r-help@r-project.org/msg17169.html :
'mode' is a mutually exclusive classification of objects according to
their basic structure. The 'atomic' modes are numeric, co | Mode, Class and Type of R objects
From: https://www.mail-archive.com/r-help@r-project.org/msg17169.html :
'mode' is a mutually exclusive classification of objects according to
their basic structure. The 'atomic' modes are numeric, complex,
character and logical. Recursive objects have modes such as 'list' or
'functio... | Mode, Class and Type of R objects
From: https://www.mail-archive.com/r-help@r-project.org/msg17169.html :
'mode' is a mutually exclusive classification of objects according to
their basic structure. The 'atomic' modes are numeric, co |
7,059 | Mode, Class and Type of R objects | The main difference between class and typeof is that the first can be defined by the user, but the type cannot. For example, define a list
> x<-list("a",c(1,2))
> # x is a list
> class(x)
[1] "list"
> # class can be user defined
> class(x)<-"newclass"
> class(x)
[1] "newclass"
> typeof(x)
[1] "list"
# you cannot assi... | Mode, Class and Type of R objects | The main difference between class and typeof is that the first can be defined by the user, but the type cannot. For example, define a list
> x<-list("a",c(1,2))
> # x is a list
> class(x)
[1] "list"
| Mode, Class and Type of R objects
The main difference between class and typeof is that the first can be defined by the user, but the type cannot. For example, define a list
> x<-list("a",c(1,2))
> # x is a list
> class(x)
[1] "list"
> # class can be user defined
> class(x)<-"newclass"
> class(x)
[1] "newclass"
> type... | Mode, Class and Type of R objects
The main difference between class and typeof is that the first can be defined by the user, but the type cannot. For example, define a list
> x<-list("a",c(1,2))
> # x is a list
> class(x)
[1] "list"
|
7,060 | How to visualize/understand what a neural network is doing? | Neural networks are sometimes called "differentiable function approximators". So what you can do is to differentiate any unit with respect to any other unit to see what their relationshsip is.
You can check how sensitive the error of the network is wrt to a specific input as well with this.
Then, there is something cal... | How to visualize/understand what a neural network is doing? | Neural networks are sometimes called "differentiable function approximators". So what you can do is to differentiate any unit with respect to any other unit to see what their relationshsip is.
You can | How to visualize/understand what a neural network is doing?
Neural networks are sometimes called "differentiable function approximators". So what you can do is to differentiate any unit with respect to any other unit to see what their relationshsip is.
You can check how sensitive the error of the network is wrt to a sp... | How to visualize/understand what a neural network is doing?
Neural networks are sometimes called "differentiable function approximators". So what you can do is to differentiate any unit with respect to any other unit to see what their relationshsip is.
You can |
7,061 | How to visualize/understand what a neural network is doing? | Estimate feature importance by randomly bumping every value of a single feature, and recording how your overall fitness function degrades.
So if your first feature $x_{1,i}$ is continuously-valued and scaled to $[0,1]$, then you might add $rand(0,1)-0.5$ to each training example's value for the first feature. Then look... | How to visualize/understand what a neural network is doing? | Estimate feature importance by randomly bumping every value of a single feature, and recording how your overall fitness function degrades.
So if your first feature $x_{1,i}$ is continuously-valued and | How to visualize/understand what a neural network is doing?
Estimate feature importance by randomly bumping every value of a single feature, and recording how your overall fitness function degrades.
So if your first feature $x_{1,i}$ is continuously-valued and scaled to $[0,1]$, then you might add $rand(0,1)-0.5$ to ea... | How to visualize/understand what a neural network is doing?
Estimate feature importance by randomly bumping every value of a single feature, and recording how your overall fitness function degrades.
So if your first feature $x_{1,i}$ is continuously-valued and |
7,062 | How to visualize/understand what a neural network is doing? | Here's a graphical intuition for a particular kind of neural networks. At the end of that post, there's a link to R code that shows a visualization for a particular problem. Here's what that looks like: | How to visualize/understand what a neural network is doing? | Here's a graphical intuition for a particular kind of neural networks. At the end of that post, there's a link to R code that shows a visualization for a particular problem. Here's what that looks lik | How to visualize/understand what a neural network is doing?
Here's a graphical intuition for a particular kind of neural networks. At the end of that post, there's a link to R code that shows a visualization for a particular problem. Here's what that looks like: | How to visualize/understand what a neural network is doing?
Here's a graphical intuition for a particular kind of neural networks. At the end of that post, there's a link to R code that shows a visualization for a particular problem. Here's what that looks lik |
7,063 | How to visualize/understand what a neural network is doing? | Fall 2011 I took the free online Machine Learning course from Standford taught by Andrew Ng, and we visualized a neural network which was a face detector. The output was a generic face. I want to mention this for completeness, but you didn't mention this kind of application, so I am not going to dig up the details. :... | How to visualize/understand what a neural network is doing? | Fall 2011 I took the free online Machine Learning course from Standford taught by Andrew Ng, and we visualized a neural network which was a face detector. The output was a generic face. I want to me | How to visualize/understand what a neural network is doing?
Fall 2011 I took the free online Machine Learning course from Standford taught by Andrew Ng, and we visualized a neural network which was a face detector. The output was a generic face. I want to mention this for completeness, but you didn't mention this kin... | How to visualize/understand what a neural network is doing?
Fall 2011 I took the free online Machine Learning course from Standford taught by Andrew Ng, and we visualized a neural network which was a face detector. The output was a generic face. I want to me |
7,064 | How to visualize/understand what a neural network is doing? | The below mentioned method is taken from this link, visit the site for more details.
Start with a random image, i.e., arbitrarily provide values to the pixels. "Next, we do a forward pass using this image x as input to the network to compute the activation a_i(x) caused by x at some neuron i somewhere in the middle of ... | How to visualize/understand what a neural network is doing? | The below mentioned method is taken from this link, visit the site for more details.
Start with a random image, i.e., arbitrarily provide values to the pixels. "Next, we do a forward pass using this i | How to visualize/understand what a neural network is doing?
The below mentioned method is taken from this link, visit the site for more details.
Start with a random image, i.e., arbitrarily provide values to the pixels. "Next, we do a forward pass using this image x as input to the network to compute the activation a_i... | How to visualize/understand what a neural network is doing?
The below mentioned method is taken from this link, visit the site for more details.
Start with a random image, i.e., arbitrarily provide values to the pixels. "Next, we do a forward pass using this i |
7,065 | Can AIC compare across different types of model? | It depends. AIC is a function of the log likelihood. If both types of model compute the log likelihood the same way (i.e. include the same constant) then yes you can, if the models are nested.
I'm reasonably certain that glm() and lmer() don't use comparable log likelihoods.
The point about nested models is also up for... | Can AIC compare across different types of model? | It depends. AIC is a function of the log likelihood. If both types of model compute the log likelihood the same way (i.e. include the same constant) then yes you can, if the models are nested.
I'm rea | Can AIC compare across different types of model?
It depends. AIC is a function of the log likelihood. If both types of model compute the log likelihood the same way (i.e. include the same constant) then yes you can, if the models are nested.
I'm reasonably certain that glm() and lmer() don't use comparable log likeliho... | Can AIC compare across different types of model?
It depends. AIC is a function of the log likelihood. If both types of model compute the log likelihood the same way (i.e. include the same constant) then yes you can, if the models are nested.
I'm rea |
7,066 | Can AIC compare across different types of model? | This is a great question that I've been curious about for a while.
For models in the same family (ie. auto-regressive models of order k or polynomials) AIC/BIC makes a lot of sense. In other cases it's less clear. Computing the log-likelihood exactly (with the constant terms) should work, but using more complicated mod... | Can AIC compare across different types of model? | This is a great question that I've been curious about for a while.
For models in the same family (ie. auto-regressive models of order k or polynomials) AIC/BIC makes a lot of sense. In other cases it' | Can AIC compare across different types of model?
This is a great question that I've been curious about for a while.
For models in the same family (ie. auto-regressive models of order k or polynomials) AIC/BIC makes a lot of sense. In other cases it's less clear. Computing the log-likelihood exactly (with the constant t... | Can AIC compare across different types of model?
This is a great question that I've been curious about for a while.
For models in the same family (ie. auto-regressive models of order k or polynomials) AIC/BIC makes a lot of sense. In other cases it' |
7,067 | Can AIC compare across different types of model? | Note that in some cases AIC cannot even compare models of the same type, like ARIMA models with a different order of differencing. Quoting Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos:
It is important to note that these information criteria tend not to be good guides to selecting the... | Can AIC compare across different types of model? | Note that in some cases AIC cannot even compare models of the same type, like ARIMA models with a different order of differencing. Quoting Forecasting: Principles and Practice by Rob J Hyndman and Geo | Can AIC compare across different types of model?
Note that in some cases AIC cannot even compare models of the same type, like ARIMA models with a different order of differencing. Quoting Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos:
It is important to note that these information cri... | Can AIC compare across different types of model?
Note that in some cases AIC cannot even compare models of the same type, like ARIMA models with a different order of differencing. Quoting Forecasting: Principles and Practice by Rob J Hyndman and Geo |
7,068 | Model selection and cross-validation: The right way | My paper in JMLR addresses this exact question, and demonstrates why the procedure suggested in the question (or at least one very like it) results in optimistically biased performance estimates:
Gavin C. Cawley, Nicola L. C. Talbot, "On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evalu... | Model selection and cross-validation: The right way | My paper in JMLR addresses this exact question, and demonstrates why the procedure suggested in the question (or at least one very like it) results in optimistically biased performance estimates:
Gavi | Model selection and cross-validation: The right way
My paper in JMLR addresses this exact question, and demonstrates why the procedure suggested in the question (or at least one very like it) results in optimistically biased performance estimates:
Gavin C. Cawley, Nicola L. C. Talbot, "On Over-fitting in Model Selectio... | Model selection and cross-validation: The right way
My paper in JMLR addresses this exact question, and demonstrates why the procedure suggested in the question (or at least one very like it) results in optimistically biased performance estimates:
Gavi |
7,069 | Model selection and cross-validation: The right way | Using a SVM with fixed hyperparameters ($\gamma$ and $C$) is a machine learning algorithm.
A procedure that optimizes these hyperparameters and trains a SVM with these is also just a machine learning algorithm.
Instead of only optimizing the internal parameters of the SVM (the support vectors) it also optimizes the hy... | Model selection and cross-validation: The right way | Using a SVM with fixed hyperparameters ($\gamma$ and $C$) is a machine learning algorithm.
A procedure that optimizes these hyperparameters and trains a SVM with these is also just a machine learning | Model selection and cross-validation: The right way
Using a SVM with fixed hyperparameters ($\gamma$ and $C$) is a machine learning algorithm.
A procedure that optimizes these hyperparameters and trains a SVM with these is also just a machine learning algorithm.
Instead of only optimizing the internal parameters of th... | Model selection and cross-validation: The right way
Using a SVM with fixed hyperparameters ($\gamma$ and $C$) is a machine learning algorithm.
A procedure that optimizes these hyperparameters and trains a SVM with these is also just a machine learning |
7,070 | What are the benefits of using ReLU over softplus as activation functions? | I found an answer to your question in the Section 6.3.3 of the Deep Learning book. (Goodfellow et. al, 2016):
The use of softplus is generally discouraged. ... one might expect it to have advantage over the rectifier due to being differentiable everywhere or due to saturating less completely, but empirically it does n... | What are the benefits of using ReLU over softplus as activation functions? | I found an answer to your question in the Section 6.3.3 of the Deep Learning book. (Goodfellow et. al, 2016):
The use of softplus is generally discouraged. ... one might expect it to have advantage o | What are the benefits of using ReLU over softplus as activation functions?
I found an answer to your question in the Section 6.3.3 of the Deep Learning book. (Goodfellow et. al, 2016):
The use of softplus is generally discouraged. ... one might expect it to have advantage over the rectifier due to being differentiable... | What are the benefits of using ReLU over softplus as activation functions?
I found an answer to your question in the Section 6.3.3 of the Deep Learning book. (Goodfellow et. al, 2016):
The use of softplus is generally discouraged. ... one might expect it to have advantage o |
7,071 | What are the benefits of using ReLU over softplus as activation functions? | ReLUs can indeed be permanently switched off, particularly under high learning rates. This is a motivation behind leaky ReLU, and ELU activations, both of which have non-zero gradient almost everywhere.
Leaky ReLU is a piecewise linear function, just as for ReLU, so quick to compute. ELU has the advantage over softplus... | What are the benefits of using ReLU over softplus as activation functions? | ReLUs can indeed be permanently switched off, particularly under high learning rates. This is a motivation behind leaky ReLU, and ELU activations, both of which have non-zero gradient almost everywher | What are the benefits of using ReLU over softplus as activation functions?
ReLUs can indeed be permanently switched off, particularly under high learning rates. This is a motivation behind leaky ReLU, and ELU activations, both of which have non-zero gradient almost everywhere.
Leaky ReLU is a piecewise linear function,... | What are the benefits of using ReLU over softplus as activation functions?
ReLUs can indeed be permanently switched off, particularly under high learning rates. This is a motivation behind leaky ReLU, and ELU activations, both of which have non-zero gradient almost everywher |
7,072 | What are the benefits of using ReLU over softplus as activation functions? | The main reason ReLU works better than Softplus is that for ReLU we have the idea of sparsity in the model. This means that some of the neurons of the model output zero which does not have any effect for the next layers. this idea is something like Dropout. Neurons in hidden layers learn hidden concepts. If the input d... | What are the benefits of using ReLU over softplus as activation functions? | The main reason ReLU works better than Softplus is that for ReLU we have the idea of sparsity in the model. This means that some of the neurons of the model output zero which does not have any effect | What are the benefits of using ReLU over softplus as activation functions?
The main reason ReLU works better than Softplus is that for ReLU we have the idea of sparsity in the model. This means that some of the neurons of the model output zero which does not have any effect for the next layers. this idea is something l... | What are the benefits of using ReLU over softplus as activation functions?
The main reason ReLU works better than Softplus is that for ReLU we have the idea of sparsity in the model. This means that some of the neurons of the model output zero which does not have any effect |
7,073 | Reference: who introduced the tilde "~" notation to mean "has probability distribution..."? | Early uses
There are some earlier uses since 1961 by Ingram Olkin with others.
Olkin, Ingram, and Robert F. Tate. "Multivariate correlation models with mixed discrete and continuous variables." The Annals of Mathematical Statistics (1961): 448-465.
$X \sim F(x)$ means that $x$ is distributed according to the d.f. $F(... | Reference: who introduced the tilde "~" notation to mean "has probability distribution..."? | Early uses
There are some earlier uses since 1961 by Ingram Olkin with others.
Olkin, Ingram, and Robert F. Tate. "Multivariate correlation models with mixed discrete and continuous variables." The A | Reference: who introduced the tilde "~" notation to mean "has probability distribution..."?
Early uses
There are some earlier uses since 1961 by Ingram Olkin with others.
Olkin, Ingram, and Robert F. Tate. "Multivariate correlation models with mixed discrete and continuous variables." The Annals of Mathematical Statis... | Reference: who introduced the tilde "~" notation to mean "has probability distribution..."?
Early uses
There are some earlier uses since 1961 by Ingram Olkin with others.
Olkin, Ingram, and Robert F. Tate. "Multivariate correlation models with mixed discrete and continuous variables." The A |
7,074 | How would PCA help with a k-means clustering analysis? | PCA is not a clustering method. But sometimes it helps to reveal clusters.
Let's assume you have 10-dimensional Normal distributions with mean $0_{10}$ (vector of zeros) and some covariance matrix with 3 directions having bigger variance than others. Applying principal component analysis with 3 components will give you... | How would PCA help with a k-means clustering analysis? | PCA is not a clustering method. But sometimes it helps to reveal clusters.
Let's assume you have 10-dimensional Normal distributions with mean $0_{10}$ (vector of zeros) and some covariance matrix wit | How would PCA help with a k-means clustering analysis?
PCA is not a clustering method. But sometimes it helps to reveal clusters.
Let's assume you have 10-dimensional Normal distributions with mean $0_{10}$ (vector of zeros) and some covariance matrix with 3 directions having bigger variance than others. Applying princ... | How would PCA help with a k-means clustering analysis?
PCA is not a clustering method. But sometimes it helps to reveal clusters.
Let's assume you have 10-dimensional Normal distributions with mean $0_{10}$ (vector of zeros) and some covariance matrix wit |
7,075 | How would PCA help with a k-means clustering analysis? | to my opinion:
PCA extracts main non-correlated features (x) that explains most of the variety in y=f(x).
Cluster analysis mostly extracts groups of samples & their labels (or y results in y=f(x) ) | How would PCA help with a k-means clustering analysis? | to my opinion:
PCA extracts main non-correlated features (x) that explains most of the variety in y=f(x).
Cluster analysis mostly extracts groups of samples & their labels (or y results in y=f(x) ) | How would PCA help with a k-means clustering analysis?
to my opinion:
PCA extracts main non-correlated features (x) that explains most of the variety in y=f(x).
Cluster analysis mostly extracts groups of samples & their labels (or y results in y=f(x) ) | How would PCA help with a k-means clustering analysis?
to my opinion:
PCA extracts main non-correlated features (x) that explains most of the variety in y=f(x).
Cluster analysis mostly extracts groups of samples & their labels (or y results in y=f(x) ) |
7,076 | Why does glmer not achieve the maximum likelihood (as verified by applying further generic optimization)? | Setting a high value of nAGQ in the glmer call made the MLEs from the two methods equivalent. The default precision of glmer was not very good. This settles the issue.
glmer(cbind(y,N-y)~1+(1|id),family=binomial,nAGQ=20)
See @SteveWalker's answer here Why can't I match glmer (family=binomial) output with manual implem... | Why does glmer not achieve the maximum likelihood (as verified by applying further generic optimizat | Setting a high value of nAGQ in the glmer call made the MLEs from the two methods equivalent. The default precision of glmer was not very good. This settles the issue.
glmer(cbind(y,N-y)~1+(1|id),fami | Why does glmer not achieve the maximum likelihood (as verified by applying further generic optimization)?
Setting a high value of nAGQ in the glmer call made the MLEs from the two methods equivalent. The default precision of glmer was not very good. This settles the issue.
glmer(cbind(y,N-y)~1+(1|id),family=binomial,nA... | Why does glmer not achieve the maximum likelihood (as verified by applying further generic optimizat
Setting a high value of nAGQ in the glmer call made the MLEs from the two methods equivalent. The default precision of glmer was not very good. This settles the issue.
glmer(cbind(y,N-y)~1+(1|id),fami |
7,077 | What is your favorite layman's explanation for a difficult statistical concept? | A p value is a measure of how embarrassing the data are to the null hypothesis
Nicholas Maxwell, Data Matters: Conceptual Statistics for a Random World Emeryville CA: Key College Publishing, 2004. | What is your favorite layman's explanation for a difficult statistical concept? | A p value is a measure of how embarrassing the data are to the null hypothesis
Nicholas Maxwell, Data Matters: Conceptual Statistics for a Random World Emeryville CA: Key College Publishing, 2004. | What is your favorite layman's explanation for a difficult statistical concept?
A p value is a measure of how embarrassing the data are to the null hypothesis
Nicholas Maxwell, Data Matters: Conceptual Statistics for a Random World Emeryville CA: Key College Publishing, 2004. | What is your favorite layman's explanation for a difficult statistical concept?
A p value is a measure of how embarrassing the data are to the null hypothesis
Nicholas Maxwell, Data Matters: Conceptual Statistics for a Random World Emeryville CA: Key College Publishing, 2004. |
7,078 | What is your favorite layman's explanation for a difficult statistical concept? | If you carved your distribution (histogram) out
of wood, and tried to balance it on
your finger, the balance point would
be the mean, no matter the shape of the distribution.
If you put a stick in the middle of
your scatter plot, and attached the
stick to each data point with a
spring, the resting point of the
stick wo... | What is your favorite layman's explanation for a difficult statistical concept? | If you carved your distribution (histogram) out
of wood, and tried to balance it on
your finger, the balance point would
be the mean, no matter the shape of the distribution.
If you put a stick in the | What is your favorite layman's explanation for a difficult statistical concept?
If you carved your distribution (histogram) out
of wood, and tried to balance it on
your finger, the balance point would
be the mean, no matter the shape of the distribution.
If you put a stick in the middle of
your scatter plot, and attach... | What is your favorite layman's explanation for a difficult statistical concept?
If you carved your distribution (histogram) out
of wood, and tried to balance it on
your finger, the balance point would
be the mean, no matter the shape of the distribution.
If you put a stick in the |
7,079 | What is your favorite layman's explanation for a difficult statistical concept? | I like to demonstrate sampling variation and essentially the Central Limit Theorem through an "in-class" exercise. Everybody in the class of say 100 students writes their age on a piece of paper. All pieces of paper are the same size and folded in the same fashion after I've calculated the average. This is the populati... | What is your favorite layman's explanation for a difficult statistical concept? | I like to demonstrate sampling variation and essentially the Central Limit Theorem through an "in-class" exercise. Everybody in the class of say 100 students writes their age on a piece of paper. All | What is your favorite layman's explanation for a difficult statistical concept?
I like to demonstrate sampling variation and essentially the Central Limit Theorem through an "in-class" exercise. Everybody in the class of say 100 students writes their age on a piece of paper. All pieces of paper are the same size and fo... | What is your favorite layman's explanation for a difficult statistical concept?
I like to demonstrate sampling variation and essentially the Central Limit Theorem through an "in-class" exercise. Everybody in the class of say 100 students writes their age on a piece of paper. All |
7,080 | What is your favorite layman's explanation for a difficult statistical concept? | I have used the drunkard's walk before for random walk, and the drunk and her dog for cointegration; they're very helpful (partially because they're amusing).
One of my favorite common examples is the Birthday Paradox (wikipedia entry), which illustrates some important concepts of probability. You can simulate this wi... | What is your favorite layman's explanation for a difficult statistical concept? | I have used the drunkard's walk before for random walk, and the drunk and her dog for cointegration; they're very helpful (partially because they're amusing).
One of my favorite common examples is the | What is your favorite layman's explanation for a difficult statistical concept?
I have used the drunkard's walk before for random walk, and the drunk and her dog for cointegration; they're very helpful (partially because they're amusing).
One of my favorite common examples is the Birthday Paradox (wikipedia entry), whi... | What is your favorite layman's explanation for a difficult statistical concept?
I have used the drunkard's walk before for random walk, and the drunk and her dog for cointegration; they're very helpful (partially because they're amusing).
One of my favorite common examples is the |
7,081 | What is your favorite layman's explanation for a difficult statistical concept? | Definitely the Monty Hall Problem. http://en.wikipedia.org/wiki/Monty_Hall_problem | What is your favorite layman's explanation for a difficult statistical concept? | Definitely the Monty Hall Problem. http://en.wikipedia.org/wiki/Monty_Hall_problem | What is your favorite layman's explanation for a difficult statistical concept?
Definitely the Monty Hall Problem. http://en.wikipedia.org/wiki/Monty_Hall_problem | What is your favorite layman's explanation for a difficult statistical concept?
Definitely the Monty Hall Problem. http://en.wikipedia.org/wiki/Monty_Hall_problem |
7,082 | What is your favorite layman's explanation for a difficult statistical concept? | 1) A good demonstration of how "random" needs to be defined in order to work out probability of certain events:
What is the chance that a random line drawn across a circle will be longer than the radius?
The question totally depends how you draw your line. Possibilities which you can describe in a real-world way for a ... | What is your favorite layman's explanation for a difficult statistical concept? | 1) A good demonstration of how "random" needs to be defined in order to work out probability of certain events:
What is the chance that a random line drawn across a circle will be longer than the radi | What is your favorite layman's explanation for a difficult statistical concept?
1) A good demonstration of how "random" needs to be defined in order to work out probability of certain events:
What is the chance that a random line drawn across a circle will be longer than the radius?
The question totally depends how you... | What is your favorite layman's explanation for a difficult statistical concept?
1) A good demonstration of how "random" needs to be defined in order to work out probability of certain events:
What is the chance that a random line drawn across a circle will be longer than the radi |
7,083 | What is your favorite layman's explanation for a difficult statistical concept? | Sam Savage's book Flaw of Averages is filled with good layman explanations of statistical concepts. In particular, he has a good explanation of Jensen's inequality. If the graph of your return on an investment is convex, i.e. it "smiles at you", then randomness is in your favor: your average return is greater than yo... | What is your favorite layman's explanation for a difficult statistical concept? | Sam Savage's book Flaw of Averages is filled with good layman explanations of statistical concepts. In particular, he has a good explanation of Jensen's inequality. If the graph of your return on an | What is your favorite layman's explanation for a difficult statistical concept?
Sam Savage's book Flaw of Averages is filled with good layman explanations of statistical concepts. In particular, he has a good explanation of Jensen's inequality. If the graph of your return on an investment is convex, i.e. it "smiles a... | What is your favorite layman's explanation for a difficult statistical concept?
Sam Savage's book Flaw of Averages is filled with good layman explanations of statistical concepts. In particular, he has a good explanation of Jensen's inequality. If the graph of your return on an |
7,084 | What is your favorite layman's explanation for a difficult statistical concept? | Along the lines of the mean as balance point, I like this view of the median as a balance point:
A Pearl: a Balanced Median Necklace | What is your favorite layman's explanation for a difficult statistical concept? | Along the lines of the mean as balance point, I like this view of the median as a balance point:
A Pearl: a Balanced Median Necklace | What is your favorite layman's explanation for a difficult statistical concept?
Along the lines of the mean as balance point, I like this view of the median as a balance point:
A Pearl: a Balanced Median Necklace | What is your favorite layman's explanation for a difficult statistical concept?
Along the lines of the mean as balance point, I like this view of the median as a balance point:
A Pearl: a Balanced Median Necklace |
7,085 | What is your favorite layman's explanation for a difficult statistical concept? | Behar et al have a collection of 25 analogies for teaching statistics.
Here are two examples:
2.9 All Models are Theoretical:
There Are No Perfect Spheres in the Universe It appears that the most common geometric form in the
universe is the sphere. But how many mathematically perfect spheres
are there in the uni... | What is your favorite layman's explanation for a difficult statistical concept? | Behar et al have a collection of 25 analogies for teaching statistics.
Here are two examples:
2.9 All Models are Theoretical:
There Are No Perfect Spheres in the Universe It appears that the most c | What is your favorite layman's explanation for a difficult statistical concept?
Behar et al have a collection of 25 analogies for teaching statistics.
Here are two examples:
2.9 All Models are Theoretical:
There Are No Perfect Spheres in the Universe It appears that the most common geometric form in the
universe i... | What is your favorite layman's explanation for a difficult statistical concept?
Behar et al have a collection of 25 analogies for teaching statistics.
Here are two examples:
2.9 All Models are Theoretical:
There Are No Perfect Spheres in the Universe It appears that the most c |
7,086 | What is your favorite layman's explanation for a difficult statistical concept? | Fun question.
Someone found out I work in biostatistics, and they asked me (basically) "Isn't statistics just a way of lying?"
(Which brings back the Mark Twain quote about Lies, Damn Lies, and Statistics.)
I tried to explain that statistics allows us to say with 100 percent precision that, given assumptions, and given... | What is your favorite layman's explanation for a difficult statistical concept? | Fun question.
Someone found out I work in biostatistics, and they asked me (basically) "Isn't statistics just a way of lying?"
(Which brings back the Mark Twain quote about Lies, Damn Lies, and Statis | What is your favorite layman's explanation for a difficult statistical concept?
Fun question.
Someone found out I work in biostatistics, and they asked me (basically) "Isn't statistics just a way of lying?"
(Which brings back the Mark Twain quote about Lies, Damn Lies, and Statistics.)
I tried to explain that statistic... | What is your favorite layman's explanation for a difficult statistical concept?
Fun question.
Someone found out I work in biostatistics, and they asked me (basically) "Isn't statistics just a way of lying?"
(Which brings back the Mark Twain quote about Lies, Damn Lies, and Statis |
7,087 | Why are log probabilities useful? | The log of $1$ is just $0$ and the limit as $x$ approaches $0$ (from the positive side) of $\log x$ is $-\infty$. So the range of values for log probabilities is $(-\infty, 0]$.
The real advantage is in the arithmetic. Log probabilities are not as easy to understand as probabilities (for most people), but every time ... | Why are log probabilities useful? | The log of $1$ is just $0$ and the limit as $x$ approaches $0$ (from the positive side) of $\log x$ is $-\infty$. So the range of values for log probabilities is $(-\infty, 0]$.
The real advantage is | Why are log probabilities useful?
The log of $1$ is just $0$ and the limit as $x$ approaches $0$ (from the positive side) of $\log x$ is $-\infty$. So the range of values for log probabilities is $(-\infty, 0]$.
The real advantage is in the arithmetic. Log probabilities are not as easy to understand as probabilities ... | Why are log probabilities useful?
The log of $1$ is just $0$ and the limit as $x$ approaches $0$ (from the positive side) of $\log x$ is $-\infty$. So the range of values for log probabilities is $(-\infty, 0]$.
The real advantage is |
7,088 | Why are log probabilities useful? | I would like to add that taking the log of a probability or probability density can often simplify certain computations, such as calculating the gradient of the density given some of its parameters. This is in particular when the density belongs to the exponential family, which often contain fewer special function call... | Why are log probabilities useful? | I would like to add that taking the log of a probability or probability density can often simplify certain computations, such as calculating the gradient of the density given some of its parameters. T | Why are log probabilities useful?
I would like to add that taking the log of a probability or probability density can often simplify certain computations, such as calculating the gradient of the density given some of its parameters. This is in particular when the density belongs to the exponential family, which often c... | Why are log probabilities useful?
I would like to add that taking the log of a probability or probability density can often simplify certain computations, such as calculating the gradient of the density given some of its parameters. T |
7,089 | Why are log probabilities useful? | As an example of the process mentioned in Greg Snow's answer: I quite often use high-level programming languages (Octave, Maxima[*], Gnuplot, Perl,...) to compute ratios between marginal likelihoods for Bayesian model comparison. If one tries to compute the ratio of marginal likelihoods directly, intermediate steps in... | Why are log probabilities useful? | As an example of the process mentioned in Greg Snow's answer: I quite often use high-level programming languages (Octave, Maxima[*], Gnuplot, Perl,...) to compute ratios between marginal likelihoods f | Why are log probabilities useful?
As an example of the process mentioned in Greg Snow's answer: I quite often use high-level programming languages (Octave, Maxima[*], Gnuplot, Perl,...) to compute ratios between marginal likelihoods for Bayesian model comparison. If one tries to compute the ratio of marginal likelihoo... | Why are log probabilities useful?
As an example of the process mentioned in Greg Snow's answer: I quite often use high-level programming languages (Octave, Maxima[*], Gnuplot, Perl,...) to compute ratios between marginal likelihoods f |
7,090 | Why are log probabilities useful? | This might not be what you are interested in, but log probabilities in statistical physics are closely related to the concepts of energy and entropy. For a physical system in equilibrium at temperature $T$ (in kelvin), the difference in energy between two microstates A and B is related to the logarithm of the probabili... | Why are log probabilities useful? | This might not be what you are interested in, but log probabilities in statistical physics are closely related to the concepts of energy and entropy. For a physical system in equilibrium at temperatur | Why are log probabilities useful?
This might not be what you are interested in, but log probabilities in statistical physics are closely related to the concepts of energy and entropy. For a physical system in equilibrium at temperature $T$ (in kelvin), the difference in energy between two microstates A and B is related... | Why are log probabilities useful?
This might not be what you are interested in, but log probabilities in statistical physics are closely related to the concepts of energy and entropy. For a physical system in equilibrium at temperatur |
7,091 | Why does logistic regression become unstable when classes are well-separated? | It isn't correct that logistic regression in itself becomes unstable when there are separation. Separation means that there are some variables which are very good predictors, which is good, or, separation may be an artifact of too few observations/too many variables. If that is the case, the solution might be to get ... | Why does logistic regression become unstable when classes are well-separated? | It isn't correct that logistic regression in itself becomes unstable when there are separation. Separation means that there are some variables which are very good predictors, which is good, or, separ | Why does logistic regression become unstable when classes are well-separated?
It isn't correct that logistic regression in itself becomes unstable when there are separation. Separation means that there are some variables which are very good predictors, which is good, or, separation may be an artifact of too few observ... | Why does logistic regression become unstable when classes are well-separated?
It isn't correct that logistic regression in itself becomes unstable when there are separation. Separation means that there are some variables which are very good predictors, which is good, or, separ |
7,092 | Why does logistic regression become unstable when classes are well-separated? | There are good answers here from @sean501 and @kjetilbhalvorsen. You asked for an example. Consider the figure below. You might come across some situation in which the data generating process is like that depicted in panel A. If so, it is quite possible that the data you actually gather look like those in panel B. ... | Why does logistic regression become unstable when classes are well-separated? | There are good answers here from @sean501 and @kjetilbhalvorsen. You asked for an example. Consider the figure below. You might come across some situation in which the data generating process is li | Why does logistic regression become unstable when classes are well-separated?
There are good answers here from @sean501 and @kjetilbhalvorsen. You asked for an example. Consider the figure below. You might come across some situation in which the data generating process is like that depicted in panel A. If so, it is... | Why does logistic regression become unstable when classes are well-separated?
There are good answers here from @sean501 and @kjetilbhalvorsen. You asked for an example. Consider the figure below. You might come across some situation in which the data generating process is li |
7,093 | Why does logistic regression become unstable when classes are well-separated? | It means that there is a hyperplane such that on one side there are all the positive points and on the other side all the negative. The maximum likelihood solution is then flat 1 on one side and flat 0 on other side, which is 'achieved' with the logistic function by having the coefficients at infinity. | Why does logistic regression become unstable when classes are well-separated? | It means that there is a hyperplane such that on one side there are all the positive points and on the other side all the negative. The maximum likelihood solution is then flat 1 on one side and flat | Why does logistic regression become unstable when classes are well-separated?
It means that there is a hyperplane such that on one side there are all the positive points and on the other side all the negative. The maximum likelihood solution is then flat 1 on one side and flat 0 on other side, which is 'achieved' wit... | Why does logistic regression become unstable when classes are well-separated?
It means that there is a hyperplane such that on one side there are all the positive points and on the other side all the negative. The maximum likelihood solution is then flat 1 on one side and flat |
7,094 | Why does logistic regression become unstable when classes are well-separated? | What you are calling "separation" (not 'seperation') covers two different situations that end up causing the same issue – which I would not call, however, an issue of "instability" as you do.
An illustration: Survival on the Titanic
Let $DV \in (0, 1)$ be a binary dependent variable, and $SV$ a separating, independent... | Why does logistic regression become unstable when classes are well-separated? | What you are calling "separation" (not 'seperation') covers two different situations that end up causing the same issue – which I would not call, however, an issue of "instability" as you do.
An illus | Why does logistic regression become unstable when classes are well-separated?
What you are calling "separation" (not 'seperation') covers two different situations that end up causing the same issue – which I would not call, however, an issue of "instability" as you do.
An illustration: Survival on the Titanic
Let $DV ... | Why does logistic regression become unstable when classes are well-separated?
What you are calling "separation" (not 'seperation') covers two different situations that end up causing the same issue – which I would not call, however, an issue of "instability" as you do.
An illus |
7,095 | Are all simulation methods some form of Monte Carlo? | There are simulations that are not Monte Carlo. Basically, all Monte Carlo methods use the (weak) law of large numbers: The mean converges to its expectation.
Then there are Quasi Monte Carlo methods. These are simulated with a compromise of random numbers and equally spaced grids to yield faster convergece.
Simulatio... | Are all simulation methods some form of Monte Carlo? | There are simulations that are not Monte Carlo. Basically, all Monte Carlo methods use the (weak) law of large numbers: The mean converges to its expectation.
Then there are Quasi Monte Carlo methods | Are all simulation methods some form of Monte Carlo?
There are simulations that are not Monte Carlo. Basically, all Monte Carlo methods use the (weak) law of large numbers: The mean converges to its expectation.
Then there are Quasi Monte Carlo methods. These are simulated with a compromise of random numbers and equal... | Are all simulation methods some form of Monte Carlo?
There are simulations that are not Monte Carlo. Basically, all Monte Carlo methods use the (weak) law of large numbers: The mean converges to its expectation.
Then there are Quasi Monte Carlo methods |
7,096 | Are all simulation methods some form of Monte Carlo? | Monte Carlo method was the first approach to use computer simulation for statistical problems. It was developed by the John von Neumann, Stanisław Ulam, & Nicholas Metropolis team from Los Alamos laboratories that was working on the Manhattan project during the World War II. It was first described in 1949 by Metropolis... | Are all simulation methods some form of Monte Carlo? | Monte Carlo method was the first approach to use computer simulation for statistical problems. It was developed by the John von Neumann, Stanisław Ulam, & Nicholas Metropolis team from Los Alamos labo | Are all simulation methods some form of Monte Carlo?
Monte Carlo method was the first approach to use computer simulation for statistical problems. It was developed by the John von Neumann, Stanisław Ulam, & Nicholas Metropolis team from Los Alamos laboratories that was working on the Manhattan project during the World... | Are all simulation methods some form of Monte Carlo?
Monte Carlo method was the first approach to use computer simulation for statistical problems. It was developed by the John von Neumann, Stanisław Ulam, & Nicholas Metropolis team from Los Alamos labo |
7,097 | Are all simulation methods some form of Monte Carlo? | All simulation methods involve substituting random numbers into the function to find a range of values for the function.
I have never heard of that definition of simulation. For example, Wikipedia’s articles on simulation and computer simulations mention terms like random and stochastic only briefly.
A simple example ... | Are all simulation methods some form of Monte Carlo? | All simulation methods involve substituting random numbers into the function to find a range of values for the function.
I have never heard of that definition of simulation. For example, Wikipedia’s | Are all simulation methods some form of Monte Carlo?
All simulation methods involve substituting random numbers into the function to find a range of values for the function.
I have never heard of that definition of simulation. For example, Wikipedia’s articles on simulation and computer simulations mention terms like ... | Are all simulation methods some form of Monte Carlo?
All simulation methods involve substituting random numbers into the function to find a range of values for the function.
I have never heard of that definition of simulation. For example, Wikipedia’s |
7,098 | Are all simulation methods some form of Monte Carlo? | No. The simulation of a particle under a force can be done using Runge-Kutta or other deterministic algorithm, which is not Monte Carlo.
Monte Carlo is used to compute integrals (you can call it a simulation, but in the end it just computes a numerical approximation of an estimator). Again, you could use a deterministi... | Are all simulation methods some form of Monte Carlo? | No. The simulation of a particle under a force can be done using Runge-Kutta or other deterministic algorithm, which is not Monte Carlo.
Monte Carlo is used to compute integrals (you can call it a sim | Are all simulation methods some form of Monte Carlo?
No. The simulation of a particle under a force can be done using Runge-Kutta or other deterministic algorithm, which is not Monte Carlo.
Monte Carlo is used to compute integrals (you can call it a simulation, but in the end it just computes a numerical approximation ... | Are all simulation methods some form of Monte Carlo?
No. The simulation of a particle under a force can be done using Runge-Kutta or other deterministic algorithm, which is not Monte Carlo.
Monte Carlo is used to compute integrals (you can call it a sim |
7,099 | Are all simulation methods some form of Monte Carlo? | Let me take a stab at a simplified explanation. A "what-if" model is a (deterministic) simulation. Say you have a complex system, like a widget processing plant. You want to be able to estimate some performance parameter, say cost. You build a mathematical model of the plant and then select various assumptions for ... | Are all simulation methods some form of Monte Carlo? | Let me take a stab at a simplified explanation. A "what-if" model is a (deterministic) simulation. Say you have a complex system, like a widget processing plant. You want to be able to estimate som | Are all simulation methods some form of Monte Carlo?
Let me take a stab at a simplified explanation. A "what-if" model is a (deterministic) simulation. Say you have a complex system, like a widget processing plant. You want to be able to estimate some performance parameter, say cost. You build a mathematical model ... | Are all simulation methods some form of Monte Carlo?
Let me take a stab at a simplified explanation. A "what-if" model is a (deterministic) simulation. Say you have a complex system, like a widget processing plant. You want to be able to estimate som |
7,100 | Are all simulation methods some form of Monte Carlo? | In game theory, especially, approaches that use randomness in the simulations are called monte carlo techniques. It is typically used as part of Monte Carlo Tree Search (MCTS) in modern programs.
(The original question did not make a distinction between "monte carlo algorithm" and "monte carlo method", which may explai... | Are all simulation methods some form of Monte Carlo? | In game theory, especially, approaches that use randomness in the simulations are called monte carlo techniques. It is typically used as part of Monte Carlo Tree Search (MCTS) in modern programs.
(The | Are all simulation methods some form of Monte Carlo?
In game theory, especially, approaches that use randomness in the simulations are called monte carlo techniques. It is typically used as part of Monte Carlo Tree Search (MCTS) in modern programs.
(The original question did not make a distinction between "monte carlo ... | Are all simulation methods some form of Monte Carlo?
In game theory, especially, approaches that use randomness in the simulations are called monte carlo techniques. It is typically used as part of Monte Carlo Tree Search (MCTS) in modern programs.
(The |
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