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 |
|---|---|---|---|---|---|---|
15,401 | Strategy for editing comma separated value (CSV) files | I like Gnumeric because it does not try to be so much idiot-resistant as others (it doesn't shout about lost functionality) and works with large data... yet I think it is Linux-only. | Strategy for editing comma separated value (CSV) files | I like Gnumeric because it does not try to be so much idiot-resistant as others (it doesn't shout about lost functionality) and works with large data... yet I think it is Linux-only. | Strategy for editing comma separated value (CSV) files
I like Gnumeric because it does not try to be so much idiot-resistant as others (it doesn't shout about lost functionality) and works with large data... yet I think it is Linux-only. | Strategy for editing comma separated value (CSV) files
I like Gnumeric because it does not try to be so much idiot-resistant as others (it doesn't shout about lost functionality) and works with large data... yet I think it is Linux-only. |
15,402 | Strategy for editing comma separated value (CSV) files | Just use Ron's Editor. Its just like Excel without the 'help'.
From the site:
Ron's Editor is a powerful tabular text, or CSV, editor. It can open
any format of separated text, including the standard comma and tab
separated files (CSV and TSV), and allows total control over their
content and structure.
Not only ... | Strategy for editing comma separated value (CSV) files | Just use Ron's Editor. Its just like Excel without the 'help'.
From the site:
Ron's Editor is a powerful tabular text, or CSV, editor. It can open
any format of separated text, including the standa | Strategy for editing comma separated value (CSV) files
Just use Ron's Editor. Its just like Excel without the 'help'.
From the site:
Ron's Editor is a powerful tabular text, or CSV, editor. It can open
any format of separated text, including the standard comma and tab
separated files (CSV and TSV), and allows tota... | Strategy for editing comma separated value (CSV) files
Just use Ron's Editor. Its just like Excel without the 'help'.
From the site:
Ron's Editor is a powerful tabular text, or CSV, editor. It can open
any format of separated text, including the standa |
15,403 | Strategy for editing comma separated value (CSV) files | I personally like to use the idea of "relational database" to manage CSV files. CSV files are good for exchange data, but contains no business logic. My experience of working with CSV is "there are many iterations with business to refine the analysis". Working with only plain text files (CSV) will pose many challenges.... | Strategy for editing comma separated value (CSV) files | I personally like to use the idea of "relational database" to manage CSV files. CSV files are good for exchange data, but contains no business logic. My experience of working with CSV is "there are ma | Strategy for editing comma separated value (CSV) files
I personally like to use the idea of "relational database" to manage CSV files. CSV files are good for exchange data, but contains no business logic. My experience of working with CSV is "there are many iterations with business to refine the analysis". Working with... | Strategy for editing comma separated value (CSV) files
I personally like to use the idea of "relational database" to manage CSV files. CSV files are good for exchange data, but contains no business logic. My experience of working with CSV is "there are ma |
15,404 | Strategy for editing comma separated value (CSV) files | If you use Excel's "Import Data" feature, it will give you option of selecting the data type for each column. You can select all the columns and use the "Text" data type. | Strategy for editing comma separated value (CSV) files | If you use Excel's "Import Data" feature, it will give you option of selecting the data type for each column. You can select all the columns and use the "Text" data type. | Strategy for editing comma separated value (CSV) files
If you use Excel's "Import Data" feature, it will give you option of selecting the data type for each column. You can select all the columns and use the "Text" data type. | Strategy for editing comma separated value (CSV) files
If you use Excel's "Import Data" feature, it will give you option of selecting the data type for each column. You can select all the columns and use the "Text" data type. |
15,405 | dropout: forward prop VS back prop in machine learning Neural Network | Yes, the neurons are considered zero during backpropagation as well. Otherwise dropout wouldn't do anything! Remember that forward propagation during training is only used to set up the network for backpropagation, where the network is actually modified (as well as for tracking training error and such).
In general, it'... | dropout: forward prop VS back prop in machine learning Neural Network | Yes, the neurons are considered zero during backpropagation as well. Otherwise dropout wouldn't do anything! Remember that forward propagation during training is only used to set up the network for ba | dropout: forward prop VS back prop in machine learning Neural Network
Yes, the neurons are considered zero during backpropagation as well. Otherwise dropout wouldn't do anything! Remember that forward propagation during training is only used to set up the network for backpropagation, where the network is actually modif... | dropout: forward prop VS back prop in machine learning Neural Network
Yes, the neurons are considered zero during backpropagation as well. Otherwise dropout wouldn't do anything! Remember that forward propagation during training is only used to set up the network for ba |
15,406 | Generalized Additive Model Python Libraries | I've written a Python implementation of GAMs using penalized B-splines.
check it out here: https://github.com/dswah/pyGAM
I've included lots of link functions, distributions and features. | Generalized Additive Model Python Libraries | I've written a Python implementation of GAMs using penalized B-splines.
check it out here: https://github.com/dswah/pyGAM
I've included lots of link functions, distributions and features. | Generalized Additive Model Python Libraries
I've written a Python implementation of GAMs using penalized B-splines.
check it out here: https://github.com/dswah/pyGAM
I've included lots of link functions, distributions and features. | Generalized Additive Model Python Libraries
I've written a Python implementation of GAMs using penalized B-splines.
check it out here: https://github.com/dswah/pyGAM
I've included lots of link functions, distributions and features. |
15,407 | Generalized Additive Model Python Libraries | Another option for quick experimentation with GAM models is the package https://github.com/malmgrek/gammy.
The emphasis is on Bayesian modeling of the GAM coefficients as well as easy extensibility on custom basis functions. Currently e.g. Gaussian processes, B-splines, as well as different trivial constructs. | Generalized Additive Model Python Libraries | Another option for quick experimentation with GAM models is the package https://github.com/malmgrek/gammy.
The emphasis is on Bayesian modeling of the GAM coefficients as well as easy extensibility on | Generalized Additive Model Python Libraries
Another option for quick experimentation with GAM models is the package https://github.com/malmgrek/gammy.
The emphasis is on Bayesian modeling of the GAM coefficients as well as easy extensibility on custom basis functions. Currently e.g. Gaussian processes, B-splines, as we... | Generalized Additive Model Python Libraries
Another option for quick experimentation with GAM models is the package https://github.com/malmgrek/gammy.
The emphasis is on Bayesian modeling of the GAM coefficients as well as easy extensibility on |
15,408 | Generalized Additive Model Python Libraries | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
Another recent development are neural additive models ... | Generalized Additive Model Python Libraries | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| Generalized Additive Model Python Libraries
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
Another re... | Generalized Additive Model Python Libraries
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
15,409 | How do I interpret this fitted vs residuals plot? | As @IrishStat commented you need to check your observed values against your errors to see if there are issues with variability. I'll come back to this towards the end.
Just so you get an idea of what we mean by heteroskedasticity: when you fit a linear model on a variable $y$ you are essentially saying that you make th... | How do I interpret this fitted vs residuals plot? | As @IrishStat commented you need to check your observed values against your errors to see if there are issues with variability. I'll come back to this towards the end.
Just so you get an idea of what | How do I interpret this fitted vs residuals plot?
As @IrishStat commented you need to check your observed values against your errors to see if there are issues with variability. I'll come back to this towards the end.
Just so you get an idea of what we mean by heteroskedasticity: when you fit a linear model on a variab... | How do I interpret this fitted vs residuals plot?
As @IrishStat commented you need to check your observed values against your errors to see if there are issues with variability. I'll come back to this towards the end.
Just so you get an idea of what |
15,410 | How do I interpret this fitted vs residuals plot? | Your question seems to be about heteroscedasticity (because you mentioned it by name and added the tag), but your explicit question (e.g., in the title and) ending your post is more general, "whether my model is appropriate or not according to this plot". There is more to determining if a model is inappropriate than a... | How do I interpret this fitted vs residuals plot? | Your question seems to be about heteroscedasticity (because you mentioned it by name and added the tag), but your explicit question (e.g., in the title and) ending your post is more general, "whether | How do I interpret this fitted vs residuals plot?
Your question seems to be about heteroscedasticity (because you mentioned it by name and added the tag), but your explicit question (e.g., in the title and) ending your post is more general, "whether my model is appropriate or not according to this plot". There is more... | How do I interpret this fitted vs residuals plot?
Your question seems to be about heteroscedasticity (because you mentioned it by name and added the tag), but your explicit question (e.g., in the title and) ending your post is more general, "whether |
15,411 | Understanding the confidence band from a polynomial regression | The gray band is a confidence band for the regression line. I'm not familiar enough with ggplot2 to know for sure whether it is a 1 SE confidence band or a 95% confidence band, but I believe it is the former (Edit: evidently it is a 95% CI). A confidence band provides a representation of the uncertainty about your re... | Understanding the confidence band from a polynomial regression | The gray band is a confidence band for the regression line. I'm not familiar enough with ggplot2 to know for sure whether it is a 1 SE confidence band or a 95% confidence band, but I believe it is th | Understanding the confidence band from a polynomial regression
The gray band is a confidence band for the regression line. I'm not familiar enough with ggplot2 to know for sure whether it is a 1 SE confidence band or a 95% confidence band, but I believe it is the former (Edit: evidently it is a 95% CI). A confidence ... | Understanding the confidence band from a polynomial regression
The gray band is a confidence band for the regression line. I'm not familiar enough with ggplot2 to know for sure whether it is a 1 SE confidence band or a 95% confidence band, but I believe it is th |
15,412 | Understanding the confidence band from a polynomial regression | To add to the already existing answers, the band represents a confidence interval of the mean, but from your question you clearly are looking for a prediction interval. Prediction intervals are a range that if you drew one new point that point would theoretically be contained in the range X% of the time (where you can ... | Understanding the confidence band from a polynomial regression | To add to the already existing answers, the band represents a confidence interval of the mean, but from your question you clearly are looking for a prediction interval. Prediction intervals are a rang | Understanding the confidence band from a polynomial regression
To add to the already existing answers, the band represents a confidence interval of the mean, but from your question you clearly are looking for a prediction interval. Prediction intervals are a range that if you drew one new point that point would theoret... | Understanding the confidence band from a polynomial regression
To add to the already existing answers, the band represents a confidence interval of the mean, but from your question you clearly are looking for a prediction interval. Prediction intervals are a rang |
15,413 | Understanding the confidence band from a polynomial regression | Well, the blue line is a smooth local regression. You may control the wiggliness of the line by the span parameter (from 0 to 1). But your example is a "time-series" so try to look for some more proper methods of analysis than only fit a smooth curve (which should serve only to reveal possible trend).
According to docu... | Understanding the confidence band from a polynomial regression | Well, the blue line is a smooth local regression. You may control the wiggliness of the line by the span parameter (from 0 to 1). But your example is a "time-series" so try to look for some more prope | Understanding the confidence band from a polynomial regression
Well, the blue line is a smooth local regression. You may control the wiggliness of the line by the span parameter (from 0 to 1). But your example is a "time-series" so try to look for some more proper methods of analysis than only fit a smooth curve (which... | Understanding the confidence band from a polynomial regression
Well, the blue line is a smooth local regression. You may control the wiggliness of the line by the span parameter (from 0 to 1). But your example is a "time-series" so try to look for some more prope |
15,414 | What is tied data in the context of a rank correlation coefficient? | It means data that have the same value; for instance if you have 1,2,3,3,4 as the dataset then the two 3's are tied data. If you have 1,2,3,4,5,5,5,6,7,7 as the dataset then the 5's and the 7's are tied data. | What is tied data in the context of a rank correlation coefficient? | It means data that have the same value; for instance if you have 1,2,3,3,4 as the dataset then the two 3's are tied data. If you have 1,2,3,4,5,5,5,6,7,7 as the dataset then the 5's and the 7's are ti | What is tied data in the context of a rank correlation coefficient?
It means data that have the same value; for instance if you have 1,2,3,3,4 as the dataset then the two 3's are tied data. If you have 1,2,3,4,5,5,5,6,7,7 as the dataset then the 5's and the 7's are tied data. | What is tied data in the context of a rank correlation coefficient?
It means data that have the same value; for instance if you have 1,2,3,3,4 as the dataset then the two 3's are tied data. If you have 1,2,3,4,5,5,5,6,7,7 as the dataset then the 5's and the 7's are ti |
15,415 | What is tied data in the context of a rank correlation coefficient? | "Tied data" comes up in the context of rank-based non-parametric statistical tests.
Non-parametric tests: testing that does not assume a particular probability distribution, eg it does not assume a bell-shaped curve.
rank-based: a large class of non-parametric tests start by converting the numbers (eg "3 days", "5 day... | What is tied data in the context of a rank correlation coefficient? | "Tied data" comes up in the context of rank-based non-parametric statistical tests.
Non-parametric tests: testing that does not assume a particular probability distribution, eg it does not assume a be | What is tied data in the context of a rank correlation coefficient?
"Tied data" comes up in the context of rank-based non-parametric statistical tests.
Non-parametric tests: testing that does not assume a particular probability distribution, eg it does not assume a bell-shaped curve.
rank-based: a large class of non-p... | What is tied data in the context of a rank correlation coefficient?
"Tied data" comes up in the context of rank-based non-parametric statistical tests.
Non-parametric tests: testing that does not assume a particular probability distribution, eg it does not assume a be |
15,416 | What is tied data in the context of a rank correlation coefficient? | It's simply two identical data values, such as observing 7 twice in the same data set.
This comes up in the context of statistical methods that assume data has a continuous and so identical measurements are impossible (or technically, the probability identical values is zero). Practical complications arise when these m... | What is tied data in the context of a rank correlation coefficient? | It's simply two identical data values, such as observing 7 twice in the same data set.
This comes up in the context of statistical methods that assume data has a continuous and so identical measuremen | What is tied data in the context of a rank correlation coefficient?
It's simply two identical data values, such as observing 7 twice in the same data set.
This comes up in the context of statistical methods that assume data has a continuous and so identical measurements are impossible (or technically, the probability i... | What is tied data in the context of a rank correlation coefficient?
It's simply two identical data values, such as observing 7 twice in the same data set.
This comes up in the context of statistical methods that assume data has a continuous and so identical measuremen |
15,417 | What is tied data in the context of a rank correlation coefficient? | The question is of fundamental importance:
What is a tied observation/data/pair ?
Altough often mentioned only in nonparametric methods, this notion is independent of nonparametric methods. It is mentioned in nonparametric methods because this situation will cause calculation complication in obtaining the statistics u... | What is tied data in the context of a rank correlation coefficient? | The question is of fundamental importance:
What is a tied observation/data/pair ?
Altough often mentioned only in nonparametric methods, this notion is independent of nonparametric methods. It is men | What is tied data in the context of a rank correlation coefficient?
The question is of fundamental importance:
What is a tied observation/data/pair ?
Altough often mentioned only in nonparametric methods, this notion is independent of nonparametric methods. It is mentioned in nonparametric methods because this situati... | What is tied data in the context of a rank correlation coefficient?
The question is of fundamental importance:
What is a tied observation/data/pair ?
Altough often mentioned only in nonparametric methods, this notion is independent of nonparametric methods. It is men |
15,418 | Can the empirical Hessian of an M-estimator be indefinite? | I think you're right. Let's distill your argument to its essence:
$\widehat \theta_N$ minimizes the function $Q$ defined as $Q(\theta) = {1 \over N}\sum_{i=1}^N q(w_i,\theta).$
Let $H$ be the Hessian of $Q$, whence $H(\theta) = \frac{\partial^2 Q}{\partial \theta_i \partial \theta_j}$ by definition and this in turn, ... | Can the empirical Hessian of an M-estimator be indefinite? | I think you're right. Let's distill your argument to its essence:
$\widehat \theta_N$ minimizes the function $Q$ defined as $Q(\theta) = {1 \over N}\sum_{i=1}^N q(w_i,\theta).$
Let $H$ be the Hessia | Can the empirical Hessian of an M-estimator be indefinite?
I think you're right. Let's distill your argument to its essence:
$\widehat \theta_N$ minimizes the function $Q$ defined as $Q(\theta) = {1 \over N}\sum_{i=1}^N q(w_i,\theta).$
Let $H$ be the Hessian of $Q$, whence $H(\theta) = \frac{\partial^2 Q}{\partial \t... | Can the empirical Hessian of an M-estimator be indefinite?
I think you're right. Let's distill your argument to its essence:
$\widehat \theta_N$ minimizes the function $Q$ defined as $Q(\theta) = {1 \over N}\sum_{i=1}^N q(w_i,\theta).$
Let $H$ be the Hessia |
15,419 | Can the empirical Hessian of an M-estimator be indefinite? | The quotation in full can be found here. The estimate $\hat{\theta}_N$ is the solution of minimization problem (page 344):
\begin{align}
\min_{\theta\in \Theta}N^{-1}\sum_{i=1}^Nq(w_i,\theta)
\end{align}
If the solution $\hat{\theta}_N$ is interior point of $\Theta$, objective function is twice differentiable and gradi... | Can the empirical Hessian of an M-estimator be indefinite? | The quotation in full can be found here. The estimate $\hat{\theta}_N$ is the solution of minimization problem (page 344):
\begin{align}
\min_{\theta\in \Theta}N^{-1}\sum_{i=1}^Nq(w_i,\theta)
\end{ali | Can the empirical Hessian of an M-estimator be indefinite?
The quotation in full can be found here. The estimate $\hat{\theta}_N$ is the solution of minimization problem (page 344):
\begin{align}
\min_{\theta\in \Theta}N^{-1}\sum_{i=1}^Nq(w_i,\theta)
\end{align}
If the solution $\hat{\theta}_N$ is interior point of $\T... | Can the empirical Hessian of an M-estimator be indefinite?
The quotation in full can be found here. The estimate $\hat{\theta}_N$ is the solution of minimization problem (page 344):
\begin{align}
\min_{\theta\in \Theta}N^{-1}\sum_{i=1}^Nq(w_i,\theta)
\end{ali |
15,420 | Can the empirical Hessian of an M-estimator be indefinite? | The hessian is indefinite at a saddle point. It’s possible that this may be the only stationary point in the interior of the parameter space.
Update: Let me elaborate. First, let’s assume that the empirical Hessian exists everywhere.
If $\hat{\theta}_n$ is a local (or even global) minimum of $\sum_i q(w_i, \cdot)$ an... | Can the empirical Hessian of an M-estimator be indefinite? | The hessian is indefinite at a saddle point. It’s possible that this may be the only stationary point in the interior of the parameter space.
Update: Let me elaborate. First, let’s assume that the e | Can the empirical Hessian of an M-estimator be indefinite?
The hessian is indefinite at a saddle point. It’s possible that this may be the only stationary point in the interior of the parameter space.
Update: Let me elaborate. First, let’s assume that the empirical Hessian exists everywhere.
If $\hat{\theta}_n$ is a ... | Can the empirical Hessian of an M-estimator be indefinite?
The hessian is indefinite at a saddle point. It’s possible that this may be the only stationary point in the interior of the parameter space.
Update: Let me elaborate. First, let’s assume that the e |
15,421 | Can the empirical Hessian of an M-estimator be indefinite? | There's been a lot of beating around the bush in this thread regarding whether the Hessian has to be positive (semi)definite at a local minimum. So I will make a clear statement on that.
Presuming the objective function and all constraint functions are twice continuously differentiable, then at any local minimum, the ... | Can the empirical Hessian of an M-estimator be indefinite? | There's been a lot of beating around the bush in this thread regarding whether the Hessian has to be positive (semi)definite at a local minimum. So I will make a clear statement on that.
Presuming th | Can the empirical Hessian of an M-estimator be indefinite?
There's been a lot of beating around the bush in this thread regarding whether the Hessian has to be positive (semi)definite at a local minimum. So I will make a clear statement on that.
Presuming the objective function and all constraint functions are twice c... | Can the empirical Hessian of an M-estimator be indefinite?
There's been a lot of beating around the bush in this thread regarding whether the Hessian has to be positive (semi)definite at a local minimum. So I will make a clear statement on that.
Presuming th |
15,422 | Can the empirical Hessian of an M-estimator be indefinite? | The positive answers above are true but they leave out the crucial identification assumption - if your model is not identified (or if it is only set identified) you might indeed, as Wooldridge correctly indicated, find yourself with a non-PSD empirical Hessian. Just run some non-toy psychometric / econometric model and... | Can the empirical Hessian of an M-estimator be indefinite? | The positive answers above are true but they leave out the crucial identification assumption - if your model is not identified (or if it is only set identified) you might indeed, as Wooldridge correct | Can the empirical Hessian of an M-estimator be indefinite?
The positive answers above are true but they leave out the crucial identification assumption - if your model is not identified (or if it is only set identified) you might indeed, as Wooldridge correctly indicated, find yourself with a non-PSD empirical Hessian.... | Can the empirical Hessian of an M-estimator be indefinite?
The positive answers above are true but they leave out the crucial identification assumption - if your model is not identified (or if it is only set identified) you might indeed, as Wooldridge correct |
15,423 | Are over-dispersion tests in GLMs actually *useful*? | In principle, I actually agree that 99% of the time, it's better to just use the more flexible model. With that said, here are two and a half arguments for why you might not.
(1) Less flexible means more efficient estimates. Given that variance parameters tend to be less stable than mean parameters, your assumption of... | Are over-dispersion tests in GLMs actually *useful*? | In principle, I actually agree that 99% of the time, it's better to just use the more flexible model. With that said, here are two and a half arguments for why you might not.
(1) Less flexible means | Are over-dispersion tests in GLMs actually *useful*?
In principle, I actually agree that 99% of the time, it's better to just use the more flexible model. With that said, here are two and a half arguments for why you might not.
(1) Less flexible means more efficient estimates. Given that variance parameters tend to be... | Are over-dispersion tests in GLMs actually *useful*?
In principle, I actually agree that 99% of the time, it's better to just use the more flexible model. With that said, here are two and a half arguments for why you might not.
(1) Less flexible means |
15,424 | Are over-dispersion tests in GLMs actually *useful*? | Although this is my own question, I'm also going to post my own two-cents as an answer, so that we add to the number of perspectives on this question. The issue here is whether or not it is sensible to initially fit a one-parameter distribution to data. When you use a one-parameter distribution (such as the Poisson G... | Are over-dispersion tests in GLMs actually *useful*? | Although this is my own question, I'm also going to post my own two-cents as an answer, so that we add to the number of perspectives on this question. The issue here is whether or not it is sensible | Are over-dispersion tests in GLMs actually *useful*?
Although this is my own question, I'm also going to post my own two-cents as an answer, so that we add to the number of perspectives on this question. The issue here is whether or not it is sensible to initially fit a one-parameter distribution to data. When you us... | Are over-dispersion tests in GLMs actually *useful*?
Although this is my own question, I'm also going to post my own two-cents as an answer, so that we add to the number of perspectives on this question. The issue here is whether or not it is sensible |
15,425 | Summary of a GAM fit | The way the output of this approach to fitting GAMs is structured is to group the linear parts of the smoothers in with the other parametric terms. Notice Private has an entry in the first table but it's entry is empty in the second. This is because Private is a strictly parametric term; it is a factor variable and hen... | Summary of a GAM fit | The way the output of this approach to fitting GAMs is structured is to group the linear parts of the smoothers in with the other parametric terms. Notice Private has an entry in the first table but i | Summary of a GAM fit
The way the output of this approach to fitting GAMs is structured is to group the linear parts of the smoothers in with the other parametric terms. Notice Private has an entry in the first table but it's entry is empty in the second. This is because Private is a strictly parametric term; it is a fa... | Summary of a GAM fit
The way the output of this approach to fitting GAMs is structured is to group the linear parts of the smoothers in with the other parametric terms. Notice Private has an entry in the first table but i |
15,426 | How to use XGboost.cv with hyperparameters optimization? | This is how I have trained a xgboost classifier with a 5-fold cross-validation to optimize the F1 score using randomized search for hyperparameter optimization.
Note that X and y here should be pandas dataframes.
from scipy import stats
from xgboost import XGBClassifier
from sklearn.model_selection import RandomizedSea... | How to use XGboost.cv with hyperparameters optimization? | This is how I have trained a xgboost classifier with a 5-fold cross-validation to optimize the F1 score using randomized search for hyperparameter optimization.
Note that X and y here should be pandas | How to use XGboost.cv with hyperparameters optimization?
This is how I have trained a xgboost classifier with a 5-fold cross-validation to optimize the F1 score using randomized search for hyperparameter optimization.
Note that X and y here should be pandas dataframes.
from scipy import stats
from xgboost import XGBCla... | How to use XGboost.cv with hyperparameters optimization?
This is how I have trained a xgboost classifier with a 5-fold cross-validation to optimize the F1 score using randomized search for hyperparameter optimization.
Note that X and y here should be pandas |
15,427 | How to use XGboost.cv with hyperparameters optimization? | I don't have enough reputation to make a comment on @darXider's answer. So I add an "answer" to make comments.
Why do you need for train_index, test_index in folds: since clf is already doing cross-validation to pick the best set of hyper-parameter values?
In your code, it looks like you perform CV for each of the five... | How to use XGboost.cv with hyperparameters optimization? | I don't have enough reputation to make a comment on @darXider's answer. So I add an "answer" to make comments.
Why do you need for train_index, test_index in folds: since clf is already doing cross-va | How to use XGboost.cv with hyperparameters optimization?
I don't have enough reputation to make a comment on @darXider's answer. So I add an "answer" to make comments.
Why do you need for train_index, test_index in folds: since clf is already doing cross-validation to pick the best set of hyper-parameter values?
In you... | How to use XGboost.cv with hyperparameters optimization?
I don't have enough reputation to make a comment on @darXider's answer. So I add an "answer" to make comments.
Why do you need for train_index, test_index in folds: since clf is already doing cross-va |
15,428 | Supervised dimensionality reduction | The most standard linear method of supervised dimensionality reduction is called linear discriminant analysis (LDA). It is designed to find low-dimensional projection that maximizes class separation. You can find a lot of information about it under our discriminant-analysis tag, and in any machine learning textbook suc... | Supervised dimensionality reduction | The most standard linear method of supervised dimensionality reduction is called linear discriminant analysis (LDA). It is designed to find low-dimensional projection that maximizes class separation. | Supervised dimensionality reduction
The most standard linear method of supervised dimensionality reduction is called linear discriminant analysis (LDA). It is designed to find low-dimensional projection that maximizes class separation. You can find a lot of information about it under our discriminant-analysis tag, and ... | Supervised dimensionality reduction
The most standard linear method of supervised dimensionality reduction is called linear discriminant analysis (LDA). It is designed to find low-dimensional projection that maximizes class separation. |
15,429 | Do descriptive statistics have p-values? | Your are correct. Descriptive statistics characterize the data with which you are working. To generate p-values, assumptions need to be generated. Assumptions are not descriptive. | Do descriptive statistics have p-values? | Your are correct. Descriptive statistics characterize the data with which you are working. To generate p-values, assumptions need to be generated. Assumptions are not descriptive. | Do descriptive statistics have p-values?
Your are correct. Descriptive statistics characterize the data with which you are working. To generate p-values, assumptions need to be generated. Assumptions are not descriptive. | Do descriptive statistics have p-values?
Your are correct. Descriptive statistics characterize the data with which you are working. To generate p-values, assumptions need to be generated. Assumptions are not descriptive. |
15,430 | Do descriptive statistics have p-values? | Descriptive statistics do not have p-values. Hypothesis tests, which can test whether or not a descriptive statistic equals a specific value, can have p-values. Whoever asked you to get p-values for descriptive statistics likely meant for you to get a p-value for whether or not that descriptive statistic equals 0. I re... | Do descriptive statistics have p-values? | Descriptive statistics do not have p-values. Hypothesis tests, which can test whether or not a descriptive statistic equals a specific value, can have p-values. Whoever asked you to get p-values for d | Do descriptive statistics have p-values?
Descriptive statistics do not have p-values. Hypothesis tests, which can test whether or not a descriptive statistic equals a specific value, can have p-values. Whoever asked you to get p-values for descriptive statistics likely meant for you to get a p-value for whether or not ... | Do descriptive statistics have p-values?
Descriptive statistics do not have p-values. Hypothesis tests, which can test whether or not a descriptive statistic equals a specific value, can have p-values. Whoever asked you to get p-values for d |
15,431 | Do descriptive statistics have p-values? | Almost all descriptive statistics are used in hypothesis testing too. So, it's not exclusive classification into inferential and descriptive when we talk about the metrics such as the mean and standard deviation.
For instance, the sample mean is a descriptive statistic. Yet, you can obtain its p-value if you construct ... | Do descriptive statistics have p-values? | Almost all descriptive statistics are used in hypothesis testing too. So, it's not exclusive classification into inferential and descriptive when we talk about the metrics such as the mean and standar | Do descriptive statistics have p-values?
Almost all descriptive statistics are used in hypothesis testing too. So, it's not exclusive classification into inferential and descriptive when we talk about the metrics such as the mean and standard deviation.
For instance, the sample mean is a descriptive statistic. Yet, you... | Do descriptive statistics have p-values?
Almost all descriptive statistics are used in hypothesis testing too. So, it's not exclusive classification into inferential and descriptive when we talk about the metrics such as the mean and standar |
15,432 | Do descriptive statistics have p-values? | In descriptive tables, the p-value is frequently used to check whether the randomization was successful or, in non-randomized experiments, if covariates are equally distributed among the categories of the main exposure variable. The issue is controversial because (1) you can't test whether between-group differences are... | Do descriptive statistics have p-values? | In descriptive tables, the p-value is frequently used to check whether the randomization was successful or, in non-randomized experiments, if covariates are equally distributed among the categories of | Do descriptive statistics have p-values?
In descriptive tables, the p-value is frequently used to check whether the randomization was successful or, in non-randomized experiments, if covariates are equally distributed among the categories of the main exposure variable. The issue is controversial because (1) you can't t... | Do descriptive statistics have p-values?
In descriptive tables, the p-value is frequently used to check whether the randomization was successful or, in non-randomized experiments, if covariates are equally distributed among the categories of |
15,433 | Questions on PCA: when are PCs independent? why is PCA sensitive to scaling? why are PCs constrained to be orthogonal? | Q1. Principal components are mutually orthogonal (uncorrelated) variables. Orthogonality and statistical independence are not synonyms. There is nothing special about principal components; the same is true of any variables in multivariate data analysis. If the data are multivariate normal (which is not the same as to s... | Questions on PCA: when are PCs independent? why is PCA sensitive to scaling? why are PCs constrained | Q1. Principal components are mutually orthogonal (uncorrelated) variables. Orthogonality and statistical independence are not synonyms. There is nothing special about principal components; the same is | Questions on PCA: when are PCs independent? why is PCA sensitive to scaling? why are PCs constrained to be orthogonal?
Q1. Principal components are mutually orthogonal (uncorrelated) variables. Orthogonality and statistical independence are not synonyms. There is nothing special about principal components; the same is ... | Questions on PCA: when are PCs independent? why is PCA sensitive to scaling? why are PCs constrained
Q1. Principal components are mutually orthogonal (uncorrelated) variables. Orthogonality and statistical independence are not synonyms. There is nothing special about principal components; the same is |
15,434 | How we can draw an ROC curve for decision trees? | If your classifier produces only factor outcomes (only labels) without scores, you still can draw a ROC curve. However, this ROC curve is only a point. Considering the ROC space, this point is $(x,y) = (\text{FPR}, \text{TPR})$, where $\text{FPR}$ - false positive rate and $\text{TPR}$ - true positive rate.
See more o... | How we can draw an ROC curve for decision trees? | If your classifier produces only factor outcomes (only labels) without scores, you still can draw a ROC curve. However, this ROC curve is only a point. Considering the ROC space, this point is $(x,y) | How we can draw an ROC curve for decision trees?
If your classifier produces only factor outcomes (only labels) without scores, you still can draw a ROC curve. However, this ROC curve is only a point. Considering the ROC space, this point is $(x,y) = (\text{FPR}, \text{TPR})$, where $\text{FPR}$ - false positive rate a... | How we can draw an ROC curve for decision trees?
If your classifier produces only factor outcomes (only labels) without scores, you still can draw a ROC curve. However, this ROC curve is only a point. Considering the ROC space, this point is $(x,y) |
15,435 | How we can draw an ROC curve for decision trees? | For a Decision Tree, the classes are still predicted with some level of certainty.
The answer is already given by @rapaio, but I'll expand on it a bit.
Imagine the following decision tree (it's a little bit modified version of this one)
At each node there are not only the majority class labels, but also others what ... | How we can draw an ROC curve for decision trees? | For a Decision Tree, the classes are still predicted with some level of certainty.
The answer is already given by @rapaio, but I'll expand on it a bit.
Imagine the following decision tree (it's a li | How we can draw an ROC curve for decision trees?
For a Decision Tree, the classes are still predicted with some level of certainty.
The answer is already given by @rapaio, but I'll expand on it a bit.
Imagine the following decision tree (it's a little bit modified version of this one)
At each node there are not only... | How we can draw an ROC curve for decision trees?
For a Decision Tree, the classes are still predicted with some level of certainty.
The answer is already given by @rapaio, but I'll expand on it a bit.
Imagine the following decision tree (it's a li |
15,436 | What are the issues with using percentage outcome in linear regression? | I'll address the issues relevant to either discrete or continuous possibility:
A problem with the description of the mean
You have a bounded response. But the model you're fitting isn't bounded, and so can blast right through the bound; some of your fitted values may be impossible, and predicted values eventually mus... | What are the issues with using percentage outcome in linear regression? | I'll address the issues relevant to either discrete or continuous possibility:
A problem with the description of the mean
You have a bounded response. But the model you're fitting isn't bounded, and | What are the issues with using percentage outcome in linear regression?
I'll address the issues relevant to either discrete or continuous possibility:
A problem with the description of the mean
You have a bounded response. But the model you're fitting isn't bounded, and so can blast right through the bound; some of y... | What are the issues with using percentage outcome in linear regression?
I'll address the issues relevant to either discrete or continuous possibility:
A problem with the description of the mean
You have a bounded response. But the model you're fitting isn't bounded, and |
15,437 | What are the issues with using percentage outcome in linear regression? | This is exactly the same thing as the case when the outcome is between 0 and 1, and that case is typically handled with a generalized linear model (GLM) like logistic regression. There are lots of excellent primers for logistic regression (and other GLMs) on the internet, and there is also a well-known book by Agresti ... | What are the issues with using percentage outcome in linear regression? | This is exactly the same thing as the case when the outcome is between 0 and 1, and that case is typically handled with a generalized linear model (GLM) like logistic regression. There are lots of exc | What are the issues with using percentage outcome in linear regression?
This is exactly the same thing as the case when the outcome is between 0 and 1, and that case is typically handled with a generalized linear model (GLM) like logistic regression. There are lots of excellent primers for logistic regression (and othe... | What are the issues with using percentage outcome in linear regression?
This is exactly the same thing as the case when the outcome is between 0 and 1, and that case is typically handled with a generalized linear model (GLM) like logistic regression. There are lots of exc |
15,438 | What are the issues with using percentage outcome in linear regression? | It might be worth investigating beta regression (for which I understand there is an R package), which seems well suited to such problems.
http://www.jstatsoft.org/v34/i02/paper | What are the issues with using percentage outcome in linear regression? | It might be worth investigating beta regression (for which I understand there is an R package), which seems well suited to such problems.
http://www.jstatsoft.org/v34/i02/paper | What are the issues with using percentage outcome in linear regression?
It might be worth investigating beta regression (for which I understand there is an R package), which seems well suited to such problems.
http://www.jstatsoft.org/v34/i02/paper | What are the issues with using percentage outcome in linear regression?
It might be worth investigating beta regression (for which I understand there is an R package), which seems well suited to such problems.
http://www.jstatsoft.org/v34/i02/paper |
15,439 | How to perform an ANCOVA in R | The basic tool for this is lm; note that aov is a wrapper for lm.
In particular, if you have some grouping variable (factor), $g$, and a continuous covariate $x$, the model y ~ x + g would fit a main effects ANCOVA model, while y ~ x * g would fit a model which includes interaction with the covariate. aov will take th... | How to perform an ANCOVA in R | The basic tool for this is lm; note that aov is a wrapper for lm.
In particular, if you have some grouping variable (factor), $g$, and a continuous covariate $x$, the model y ~ x + g would fit a main | How to perform an ANCOVA in R
The basic tool for this is lm; note that aov is a wrapper for lm.
In particular, if you have some grouping variable (factor), $g$, and a continuous covariate $x$, the model y ~ x + g would fit a main effects ANCOVA model, while y ~ x * g would fit a model which includes interaction with t... | How to perform an ANCOVA in R
The basic tool for this is lm; note that aov is a wrapper for lm.
In particular, if you have some grouping variable (factor), $g$, and a continuous covariate $x$, the model y ~ x + g would fit a main |
15,440 | How to perform an ANCOVA in R | I recommend getting and reading Discovering Statistics using R by Field. He has a nice section on ANCOVA.
To run ANCOVA in R load the following packages:
car
compute.es
effects
ggplot2
multcomp
pastecs
WRS
If you are using lm or aov (I use aov) make sure that you set the contrasts using the "contrasts" function befo... | How to perform an ANCOVA in R | I recommend getting and reading Discovering Statistics using R by Field. He has a nice section on ANCOVA.
To run ANCOVA in R load the following packages:
car
compute.es
effects
ggplot2
multcomp
past | How to perform an ANCOVA in R
I recommend getting and reading Discovering Statistics using R by Field. He has a nice section on ANCOVA.
To run ANCOVA in R load the following packages:
car
compute.es
effects
ggplot2
multcomp
pastecs
WRS
If you are using lm or aov (I use aov) make sure that you set the contrasts using... | How to perform an ANCOVA in R
I recommend getting and reading Discovering Statistics using R by Field. He has a nice section on ANCOVA.
To run ANCOVA in R load the following packages:
car
compute.es
effects
ggplot2
multcomp
past |
15,441 | How to perform an ANCOVA in R | Here is a complementary documentation http://goo.gl/yxUZ1R of the procedure suggested by @Butorovich. In addition, my observation is that when the covariate is binary, using summary(lm.object) would give same IV estimate as generate by Anova(lm.object, type="III"). | How to perform an ANCOVA in R | Here is a complementary documentation http://goo.gl/yxUZ1R of the procedure suggested by @Butorovich. In addition, my observation is that when the covariate is binary, using summary(lm.object) would g | How to perform an ANCOVA in R
Here is a complementary documentation http://goo.gl/yxUZ1R of the procedure suggested by @Butorovich. In addition, my observation is that when the covariate is binary, using summary(lm.object) would give same IV estimate as generate by Anova(lm.object, type="III"). | How to perform an ANCOVA in R
Here is a complementary documentation http://goo.gl/yxUZ1R of the procedure suggested by @Butorovich. In addition, my observation is that when the covariate is binary, using summary(lm.object) would g |
15,442 | Kullback–Leibler divergence between two gamma distributions | The KL divergence is a difference of integrals of the form
$$\begin{aligned}
I(a,b,c,d)&=\int_0^{\infty} \log\left(\frac{e^{-x/a}x^{b-1}}{a^b\Gamma(b)}\right) \frac{e^{-x/c}x^{d-1}}{c^d \Gamma(d)}\, \mathrm dx \\
&=-\frac{1}{a}\int_0^\infty \frac{x^d e^{-x/c}}{c^d\Gamma(d)}\, \mathrm dx
- \log(a^b\Gamma(b))\int_0^\... | Kullback–Leibler divergence between two gamma distributions | The KL divergence is a difference of integrals of the form
$$\begin{aligned}
I(a,b,c,d)&=\int_0^{\infty} \log\left(\frac{e^{-x/a}x^{b-1}}{a^b\Gamma(b)}\right) \frac{e^{-x/c}x^{d-1}}{c^d \Gamma(d)}\, \ | Kullback–Leibler divergence between two gamma distributions
The KL divergence is a difference of integrals of the form
$$\begin{aligned}
I(a,b,c,d)&=\int_0^{\infty} \log\left(\frac{e^{-x/a}x^{b-1}}{a^b\Gamma(b)}\right) \frac{e^{-x/c}x^{d-1}}{c^d \Gamma(d)}\, \mathrm dx \\
&=-\frac{1}{a}\int_0^\infty \frac{x^d e^{-x/c}}... | Kullback–Leibler divergence between two gamma distributions
The KL divergence is a difference of integrals of the form
$$\begin{aligned}
I(a,b,c,d)&=\int_0^{\infty} \log\left(\frac{e^{-x/a}x^{b-1}}{a^b\Gamma(b)}\right) \frac{e^{-x/c}x^{d-1}}{c^d \Gamma(d)}\, \ |
15,443 | Kullback–Leibler divergence between two gamma distributions | The Gamma distribution is in the exponential family because its density can be expressed as:
\begin{align}
\newcommand{\mbx}{\mathbf{x}}
\newcommand{\btheta}{\boldsymbol{\theta}}
f(\mbx \mid \btheta) &= \exp\bigl(\eta(\btheta) \cdot T(\mbx) - g(\btheta) + h(\mbx)\bigr)
\end{align}
Looking at the Gamma density function,... | Kullback–Leibler divergence between two gamma distributions | The Gamma distribution is in the exponential family because its density can be expressed as:
\begin{align}
\newcommand{\mbx}{\mathbf{x}}
\newcommand{\btheta}{\boldsymbol{\theta}}
f(\mbx \mid \btheta) | Kullback–Leibler divergence between two gamma distributions
The Gamma distribution is in the exponential family because its density can be expressed as:
\begin{align}
\newcommand{\mbx}{\mathbf{x}}
\newcommand{\btheta}{\boldsymbol{\theta}}
f(\mbx \mid \btheta) &= \exp\bigl(\eta(\btheta) \cdot T(\mbx) - g(\btheta) + h(\m... | Kullback–Leibler divergence between two gamma distributions
The Gamma distribution is in the exponential family because its density can be expressed as:
\begin{align}
\newcommand{\mbx}{\mathbf{x}}
\newcommand{\btheta}{\boldsymbol{\theta}}
f(\mbx \mid \btheta) |
15,444 | When should I *not* permit a fixed effect to vary across levels of a random effect in a mixed effects model? | I am not an expert in mixed effect modelling, but the question is much easier to answer if it is rephrased in hierarchical regression modelling context. So our observations have two indexes $P_{ij}$ and $F_{ij}$ with index $i$ representing class and $j$ members of the class. The hierarchical models let us fit linear re... | When should I *not* permit a fixed effect to vary across levels of a random effect in a mixed effect | I am not an expert in mixed effect modelling, but the question is much easier to answer if it is rephrased in hierarchical regression modelling context. So our observations have two indexes $P_{ij}$ a | When should I *not* permit a fixed effect to vary across levels of a random effect in a mixed effects model?
I am not an expert in mixed effect modelling, but the question is much easier to answer if it is rephrased in hierarchical regression modelling context. So our observations have two indexes $P_{ij}$ and $F_{ij}$... | When should I *not* permit a fixed effect to vary across levels of a random effect in a mixed effect
I am not an expert in mixed effect modelling, but the question is much easier to answer if it is rephrased in hierarchical regression modelling context. So our observations have two indexes $P_{ij}$ a |
15,445 | When should I *not* permit a fixed effect to vary across levels of a random effect in a mixed effects model? | You can think of a "Fixed effect" as a "random effect" with a variance component of zero.
So, a simple answer to why you wouldn't let fixed effect to vary, is insufficient evidence for a "large enough" variance component. The evidence should come from both the prior information and the data. This is in line with the ... | When should I *not* permit a fixed effect to vary across levels of a random effect in a mixed effect | You can think of a "Fixed effect" as a "random effect" with a variance component of zero.
So, a simple answer to why you wouldn't let fixed effect to vary, is insufficient evidence for a "large enough | When should I *not* permit a fixed effect to vary across levels of a random effect in a mixed effects model?
You can think of a "Fixed effect" as a "random effect" with a variance component of zero.
So, a simple answer to why you wouldn't let fixed effect to vary, is insufficient evidence for a "large enough" variance ... | When should I *not* permit a fixed effect to vary across levels of a random effect in a mixed effect
You can think of a "Fixed effect" as a "random effect" with a variance component of zero.
So, a simple answer to why you wouldn't let fixed effect to vary, is insufficient evidence for a "large enough |
15,446 | When should I *not* permit a fixed effect to vary across levels of a random effect in a mixed effects model? | This is quite an old question with some very good answers, however I think it can benefit from a new answer to address a more pragmatic perspective.
When should one not permit a fixed effect to vary across levels of a random effect ?
I won't address the issues already described in the other answers, instead I will re... | When should I *not* permit a fixed effect to vary across levels of a random effect in a mixed effect | This is quite an old question with some very good answers, however I think it can benefit from a new answer to address a more pragmatic perspective.
When should one not permit a fixed effect to vary | When should I *not* permit a fixed effect to vary across levels of a random effect in a mixed effects model?
This is quite an old question with some very good answers, however I think it can benefit from a new answer to address a more pragmatic perspective.
When should one not permit a fixed effect to vary across leve... | When should I *not* permit a fixed effect to vary across levels of a random effect in a mixed effect
This is quite an old question with some very good answers, however I think it can benefit from a new answer to address a more pragmatic perspective.
When should one not permit a fixed effect to vary |
15,447 | Resources for an R user who must learn SAS | 15 months ago, I started my current job as someone who had been using R exclusively for about 3 years; I had used SAS in my first-ever stats class, loathed it, and never touched it again until I started here. Here's what has been helpful for me, and what hasn't:
Helpful:
Colleagues' code. This is the single most us... | Resources for an R user who must learn SAS | 15 months ago, I started my current job as someone who had been using R exclusively for about 3 years; I had used SAS in my first-ever stats class, loathed it, and never touched it again until I start | Resources for an R user who must learn SAS
15 months ago, I started my current job as someone who had been using R exclusively for about 3 years; I had used SAS in my first-ever stats class, loathed it, and never touched it again until I started here. Here's what has been helpful for me, and what hasn't:
Helpful:
Co... | Resources for an R user who must learn SAS
15 months ago, I started my current job as someone who had been using R exclusively for about 3 years; I had used SAS in my first-ever stats class, loathed it, and never touched it again until I start |
15,448 | Resources for an R user who must learn SAS | A couple things to add to what @matt said:
In addition to SUGI (which is now renamed SAS Global Forum, and will be held this year in Las Vegas) there are numerous local and regional SAS user groups. These are smaller, more intimate, and (usually) a lot cheaper. Some local groups are even free. See here
SAS-L. This i... | Resources for an R user who must learn SAS | A couple things to add to what @matt said:
In addition to SUGI (which is now renamed SAS Global Forum, and will be held this year in Las Vegas) there are numerous local and regional SAS user groups. | Resources for an R user who must learn SAS
A couple things to add to what @matt said:
In addition to SUGI (which is now renamed SAS Global Forum, and will be held this year in Las Vegas) there are numerous local and regional SAS user groups. These are smaller, more intimate, and (usually) a lot cheaper. Some local gr... | Resources for an R user who must learn SAS
A couple things to add to what @matt said:
In addition to SUGI (which is now renamed SAS Global Forum, and will be held this year in Las Vegas) there are numerous local and regional SAS user groups. |
15,449 | Resources for an R user who must learn SAS | In addition to Matt Parkers excellent advice (particularly about reading colleagues code), the actual SAS documentation can be surprisingly helpful (once you've figured out the name of what you want):
http://support.sas.com/documentation/
And the Global Forum/SUGI proceedings are available here:
http://support.sas.com/... | Resources for an R user who must learn SAS | In addition to Matt Parkers excellent advice (particularly about reading colleagues code), the actual SAS documentation can be surprisingly helpful (once you've figured out the name of what you want): | Resources for an R user who must learn SAS
In addition to Matt Parkers excellent advice (particularly about reading colleagues code), the actual SAS documentation can be surprisingly helpful (once you've figured out the name of what you want):
http://support.sas.com/documentation/
And the Global Forum/SUGI proceedings ... | Resources for an R user who must learn SAS
In addition to Matt Parkers excellent advice (particularly about reading colleagues code), the actual SAS documentation can be surprisingly helpful (once you've figured out the name of what you want): |
15,450 | Removing borders in R plots for achieving Tufte's axis | Add bty="n" in both plot commands.
For time series, add frame.plot=FALSE for the same effect.
For fancier Tufte axes, see http://www.cl.cam.ac.uk/~sjm217/projects/graphics/ | Removing borders in R plots for achieving Tufte's axis | Add bty="n" in both plot commands.
For time series, add frame.plot=FALSE for the same effect.
For fancier Tufte axes, see http://www.cl.cam.ac.uk/~sjm217/projects/graphics/ | Removing borders in R plots for achieving Tufte's axis
Add bty="n" in both plot commands.
For time series, add frame.plot=FALSE for the same effect.
For fancier Tufte axes, see http://www.cl.cam.ac.uk/~sjm217/projects/graphics/ | Removing borders in R plots for achieving Tufte's axis
Add bty="n" in both plot commands.
For time series, add frame.plot=FALSE for the same effect.
For fancier Tufte axes, see http://www.cl.cam.ac.uk/~sjm217/projects/graphics/ |
15,451 | Removing borders in R plots for achieving Tufte's axis | This is straightforward to do, you just include the argument axes=FALSE. Consider:
x <- 1:100
y1 <- rnorm(100)
y2 <- rnorm(100) + 100
windows()
par(mar=c(5,5,5,5))
plot(x, y1, pch=0, type="b", col="red", yaxt="n", ylim=c(-8,2), ylab="", axes=F)
axis(side=2, at=c(-2,0,2))
mtext("red line", side = 2, line=2.5,... | Removing borders in R plots for achieving Tufte's axis | This is straightforward to do, you just include the argument axes=FALSE. Consider:
x <- 1:100
y1 <- rnorm(100)
y2 <- rnorm(100) + 100
windows()
par(mar=c(5,5,5,5))
plot(x, y1, pch=0, type="b", | Removing borders in R plots for achieving Tufte's axis
This is straightforward to do, you just include the argument axes=FALSE. Consider:
x <- 1:100
y1 <- rnorm(100)
y2 <- rnorm(100) + 100
windows()
par(mar=c(5,5,5,5))
plot(x, y1, pch=0, type="b", col="red", yaxt="n", ylim=c(-8,2), ylab="", axes=F)
axis(side=2... | Removing borders in R plots for achieving Tufte's axis
This is straightforward to do, you just include the argument axes=FALSE. Consider:
x <- 1:100
y1 <- rnorm(100)
y2 <- rnorm(100) + 100
windows()
par(mar=c(5,5,5,5))
plot(x, y1, pch=0, type="b", |
15,452 | Removing borders in R plots for achieving Tufte's axis | If you use
par(bty = 'n')
Before calling plot that will fix it for zoo. It might also fix it for a variety of situations where it isn't passable to the plotting command.
(Check out bty option in the par() help for other kinds of frames for the plot) | Removing borders in R plots for achieving Tufte's axis | If you use
par(bty = 'n')
Before calling plot that will fix it for zoo. It might also fix it for a variety of situations where it isn't passable to the plotting command.
(Check out bty option in th | Removing borders in R plots for achieving Tufte's axis
If you use
par(bty = 'n')
Before calling plot that will fix it for zoo. It might also fix it for a variety of situations where it isn't passable to the plotting command.
(Check out bty option in the par() help for other kinds of frames for the plot) | Removing borders in R plots for achieving Tufte's axis
If you use
par(bty = 'n')
Before calling plot that will fix it for zoo. It might also fix it for a variety of situations where it isn't passable to the plotting command.
(Check out bty option in th |
15,453 | Removing borders in R plots for achieving Tufte's axis | I am answering the more general question of removing borders in plots, without reference to Tufte.
For a histogram I did not find that btn='n' got rid of the border.
A solution that does work for histograms and should work for all types of plots is to set the line type for the border to invisible: lty="blank" | Removing borders in R plots for achieving Tufte's axis | I am answering the more general question of removing borders in plots, without reference to Tufte.
For a histogram I did not find that btn='n' got rid of the border.
A solution that does work for hist | Removing borders in R plots for achieving Tufte's axis
I am answering the more general question of removing borders in plots, without reference to Tufte.
For a histogram I did not find that btn='n' got rid of the border.
A solution that does work for histograms and should work for all types of plots is to set the line ... | Removing borders in R plots for achieving Tufte's axis
I am answering the more general question of removing borders in plots, without reference to Tufte.
For a histogram I did not find that btn='n' got rid of the border.
A solution that does work for hist |
15,454 | Is Fig 3.6 in Elements of Statistical Learning correct? | It shows a decreasing relationship between subset size $k$ and mean squared error (MSE) of the true parameters, $\beta$ and the estimates $\hat{\beta}(k)$.
The plot shows the results of alternative subset selection methods. The image caption explains the experimental design: there are 10 elements of $\beta$ which are ... | Is Fig 3.6 in Elements of Statistical Learning correct? | It shows a decreasing relationship between subset size $k$ and mean squared error (MSE) of the true parameters, $\beta$ and the estimates $\hat{\beta}(k)$.
The plot shows the results of alternative s | Is Fig 3.6 in Elements of Statistical Learning correct?
It shows a decreasing relationship between subset size $k$ and mean squared error (MSE) of the true parameters, $\beta$ and the estimates $\hat{\beta}(k)$.
The plot shows the results of alternative subset selection methods. The image caption explains the experime... | Is Fig 3.6 in Elements of Statistical Learning correct?
It shows a decreasing relationship between subset size $k$ and mean squared error (MSE) of the true parameters, $\beta$ and the estimates $\hat{\beta}(k)$.
The plot shows the results of alternative s |
15,455 | Is Fig 3.6 in Elements of Statistical Learning correct? | adding more variables to a linear model doesn't imply better estimates of the true parameters
This is not just estimating variables, but also variable selection. When you only subselect <10 variables, then you are inevitably gonna make an error.
That is why the error decreases when you are choosing a larger size for ... | Is Fig 3.6 in Elements of Statistical Learning correct? | adding more variables to a linear model doesn't imply better estimates of the true parameters
This is not just estimating variables, but also variable selection. When you only subselect <10 variables | Is Fig 3.6 in Elements of Statistical Learning correct?
adding more variables to a linear model doesn't imply better estimates of the true parameters
This is not just estimating variables, but also variable selection. When you only subselect <10 variables, then you are inevitably gonna make an error.
That is why the ... | Is Fig 3.6 in Elements of Statistical Learning correct?
adding more variables to a linear model doesn't imply better estimates of the true parameters
This is not just estimating variables, but also variable selection. When you only subselect <10 variables |
15,456 | Is Fig 3.6 in Elements of Statistical Learning correct? | There are good answers here, so I'll try to keep this brief and just add a couple points.
The point of this figure is to show how close the estimated slopes are to their true values, not how well the model predicts $y$ out of sample, or to whether inferences are valid.
adding more variables to a linear model doesn't... | Is Fig 3.6 in Elements of Statistical Learning correct? | There are good answers here, so I'll try to keep this brief and just add a couple points.
The point of this figure is to show how close the estimated slopes are to their true values, not how well the | Is Fig 3.6 in Elements of Statistical Learning correct?
There are good answers here, so I'll try to keep this brief and just add a couple points.
The point of this figure is to show how close the estimated slopes are to their true values, not how well the model predicts $y$ out of sample, or to whether inferences are ... | Is Fig 3.6 in Elements of Statistical Learning correct?
There are good answers here, so I'll try to keep this brief and just add a couple points.
The point of this figure is to show how close the estimated slopes are to their true values, not how well the |
15,457 | Is Fig 3.6 in Elements of Statistical Learning correct? | I try to give an intuitive answer without actually checking and trying to reproduce the code. No idea whether the graph is wrong, but I will explain how it corresponds to my intuition.
The question has:
"I think It shows a decreasing relationship between subset size k and mean squared error (MSE) of the true parameters... | Is Fig 3.6 in Elements of Statistical Learning correct? | I try to give an intuitive answer without actually checking and trying to reproduce the code. No idea whether the graph is wrong, but I will explain how it corresponds to my intuition.
The question ha | Is Fig 3.6 in Elements of Statistical Learning correct?
I try to give an intuitive answer without actually checking and trying to reproduce the code. No idea whether the graph is wrong, but I will explain how it corresponds to my intuition.
The question has:
"I think It shows a decreasing relationship between subset si... | Is Fig 3.6 in Elements of Statistical Learning correct?
I try to give an intuitive answer without actually checking and trying to reproduce the code. No idea whether the graph is wrong, but I will explain how it corresponds to my intuition.
The question ha |
15,458 | How to calculate overlap between empirical probability densities? | The area of overlap of two kernel density estimates may be approximated to any desired degree of accuracy.
1) Since the original KDEs have probably been evaluated over some grid, if the grid is the same for both (or can easily be made the same), the exercise could be as easy as simply taking $\min(K_1(x),K_2(x))$ at ea... | How to calculate overlap between empirical probability densities? | The area of overlap of two kernel density estimates may be approximated to any desired degree of accuracy.
1) Since the original KDEs have probably been evaluated over some grid, if the grid is the sa | How to calculate overlap between empirical probability densities?
The area of overlap of two kernel density estimates may be approximated to any desired degree of accuracy.
1) Since the original KDEs have probably been evaluated over some grid, if the grid is the same for both (or can easily be made the same), the exer... | How to calculate overlap between empirical probability densities?
The area of overlap of two kernel density estimates may be approximated to any desired degree of accuracy.
1) Since the original KDEs have probably been evaluated over some grid, if the grid is the sa |
15,459 | How to calculate overlap between empirical probability densities? | For the sake of completeness, here's how I ended up doing this in R:
# simulate two samples
a <- rnorm(100)
b <- rnorm(100, 2)
# define limits of a common grid, adding a buffer so that tails aren't cut off
lower <- min(c(a, b)) - 1
upper <- max(c(a, b)) + 1
# generate kernel densities
da <- density(a, from=lower, to... | How to calculate overlap between empirical probability densities? | For the sake of completeness, here's how I ended up doing this in R:
# simulate two samples
a <- rnorm(100)
b <- rnorm(100, 2)
# define limits of a common grid, adding a buffer so that tails aren't c | How to calculate overlap between empirical probability densities?
For the sake of completeness, here's how I ended up doing this in R:
# simulate two samples
a <- rnorm(100)
b <- rnorm(100, 2)
# define limits of a common grid, adding a buffer so that tails aren't cut off
lower <- min(c(a, b)) - 1
upper <- max(c(a, b)... | How to calculate overlap between empirical probability densities?
For the sake of completeness, here's how I ended up doing this in R:
# simulate two samples
a <- rnorm(100)
b <- rnorm(100, 2)
# define limits of a common grid, adding a buffer so that tails aren't c |
15,460 | How to calculate overlap between empirical probability densities? | First, I might be wrong but I think your solution wouldn't work in case where there is multiples points where the Kernel Density Estimates (KDE) intersect.
Second, although the overlap package was created for use with timestamp data, you can still use it to estimate the area of overlap of any two KDEs. You simply have ... | How to calculate overlap between empirical probability densities? | First, I might be wrong but I think your solution wouldn't work in case where there is multiples points where the Kernel Density Estimates (KDE) intersect.
Second, although the overlap package was cre | How to calculate overlap between empirical probability densities?
First, I might be wrong but I think your solution wouldn't work in case where there is multiples points where the Kernel Density Estimates (KDE) intersect.
Second, although the overlap package was created for use with timestamp data, you can still use it... | How to calculate overlap between empirical probability densities?
First, I might be wrong but I think your solution wouldn't work in case where there is multiples points where the Kernel Density Estimates (KDE) intersect.
Second, although the overlap package was cre |
15,461 | How to calculate overlap between empirical probability densities? | An alternative method for empirical estimation is to use the ROC (Receiver Operating Curve) technology for the estimation. The Youden threshold gives us an empirical estimate for the main point of intersection (see https://journals.lww.com/epidem/Fulltext/2005/01000/Optimal_Cut_point_and_Its_Corresponding_Youden.11.a... | How to calculate overlap between empirical probability densities? | An alternative method for empirical estimation is to use the ROC (Receiver Operating Curve) technology for the estimation. The Youden threshold gives us an empirical estimate for the main point of int | How to calculate overlap between empirical probability densities?
An alternative method for empirical estimation is to use the ROC (Receiver Operating Curve) technology for the estimation. The Youden threshold gives us an empirical estimate for the main point of intersection (see https://journals.lww.com/epidem/Fullt... | How to calculate overlap between empirical probability densities?
An alternative method for empirical estimation is to use the ROC (Receiver Operating Curve) technology for the estimation. The Youden threshold gives us an empirical estimate for the main point of int |
15,462 | Why does Natural Language Processing not fall under Machine Learning domain? [closed] | Because they are different: One does not include the other.
Yes modern NLP (Natural Language Processing) does make use of a lot of ML (Machine Learning), but that is just one group of techniques in the arsenal. For example, graph theory and search algorithms are also used a lot. As is simple text processing (Regular Ex... | Why does Natural Language Processing not fall under Machine Learning domain? [closed] | Because they are different: One does not include the other.
Yes modern NLP (Natural Language Processing) does make use of a lot of ML (Machine Learning), but that is just one group of techniques in th | Why does Natural Language Processing not fall under Machine Learning domain? [closed]
Because they are different: One does not include the other.
Yes modern NLP (Natural Language Processing) does make use of a lot of ML (Machine Learning), but that is just one group of techniques in the arsenal. For example, graph theo... | Why does Natural Language Processing not fall under Machine Learning domain? [closed]
Because they are different: One does not include the other.
Yes modern NLP (Natural Language Processing) does make use of a lot of ML (Machine Learning), but that is just one group of techniques in th |
15,463 | Why does Natural Language Processing not fall under Machine Learning domain? [closed] | I think @winwaed's answer sums it up quite well, and I agree.
However I would also add that I would say that NLP is part of a specific application area, namely text processing, and hence there is a lot of domain-specific knowledge that is contained within the techniques that are used. For the most part ML techniques ar... | Why does Natural Language Processing not fall under Machine Learning domain? [closed] | I think @winwaed's answer sums it up quite well, and I agree.
However I would also add that I would say that NLP is part of a specific application area, namely text processing, and hence there is a lo | Why does Natural Language Processing not fall under Machine Learning domain? [closed]
I think @winwaed's answer sums it up quite well, and I agree.
However I would also add that I would say that NLP is part of a specific application area, namely text processing, and hence there is a lot of domain-specific knowledge tha... | Why does Natural Language Processing not fall under Machine Learning domain? [closed]
I think @winwaed's answer sums it up quite well, and I agree.
However I would also add that I would say that NLP is part of a specific application area, namely text processing, and hence there is a lo |
15,464 | How to calculate the variance of a partition of variables | The formula is fairly straightforward if all the sub-sample have the same sample size. If you had $g$ sub-samples of size $k$ (for a total of $gk$ samples), then the variance of the combined sample depends on the mean $E_j$ and variance $V_j$ of each sub-sample:
$$ Var(X_1,\ldots,X_{gk}) = \frac{k-1}{gk-1}(\sum_{j=1}^... | How to calculate the variance of a partition of variables | The formula is fairly straightforward if all the sub-sample have the same sample size. If you had $g$ sub-samples of size $k$ (for a total of $gk$ samples), then the variance of the combined sample de | How to calculate the variance of a partition of variables
The formula is fairly straightforward if all the sub-sample have the same sample size. If you had $g$ sub-samples of size $k$ (for a total of $gk$ samples), then the variance of the combined sample depends on the mean $E_j$ and variance $V_j$ of each sub-sample:... | How to calculate the variance of a partition of variables
The formula is fairly straightforward if all the sub-sample have the same sample size. If you had $g$ sub-samples of size $k$ (for a total of $gk$ samples), then the variance of the combined sample de |
15,465 | How to calculate the variance of a partition of variables | This is simply an add-on to the answer of aniko with a rough sketch of the derivation and some python code, so all credits go to aniko.
derivation
Let $X_j \in X = \{X_1, X_2, \ldots, X_g\}$ be one of $g$ parts of the data where the number of elements in each part is $k_j = |X_j|$. We define the mean and the variance o... | How to calculate the variance of a partition of variables | This is simply an add-on to the answer of aniko with a rough sketch of the derivation and some python code, so all credits go to aniko.
derivation
Let $X_j \in X = \{X_1, X_2, \ldots, X_g\}$ be one of | How to calculate the variance of a partition of variables
This is simply an add-on to the answer of aniko with a rough sketch of the derivation and some python code, so all credits go to aniko.
derivation
Let $X_j \in X = \{X_1, X_2, \ldots, X_g\}$ be one of $g$ parts of the data where the number of elements in each pa... | How to calculate the variance of a partition of variables
This is simply an add-on to the answer of aniko with a rough sketch of the derivation and some python code, so all credits go to aniko.
derivation
Let $X_j \in X = \{X_1, X_2, \ldots, X_g\}$ be one of |
15,466 | 80% of missing data in a single variable | Are the data "missing" in the sense of being unknown or does it just mean there is no loan (so the loan amount is zero)? It sounds like the latter, in which case you need an additional binary dummy to indicate whether there is a loan. No transformation of the loan amount is needed (apart, perhaps, from a continuous r... | 80% of missing data in a single variable | Are the data "missing" in the sense of being unknown or does it just mean there is no loan (so the loan amount is zero)? It sounds like the latter, in which case you need an additional binary dummy t | 80% of missing data in a single variable
Are the data "missing" in the sense of being unknown or does it just mean there is no loan (so the loan amount is zero)? It sounds like the latter, in which case you need an additional binary dummy to indicate whether there is a loan. No transformation of the loan amount is ne... | 80% of missing data in a single variable
Are the data "missing" in the sense of being unknown or does it just mean there is no loan (so the loan amount is zero)? It sounds like the latter, in which case you need an additional binary dummy t |
15,467 | 80% of missing data in a single variable | I think you have misunderstood the suggestion of the article: mainly because the suggestion makes no sense. You would then have two problems: how to recode a variable and its values are still missing. What was probably suggested was to create a missingness indicator.
A somewhat relevant approach to handling missing da... | 80% of missing data in a single variable | I think you have misunderstood the suggestion of the article: mainly because the suggestion makes no sense. You would then have two problems: how to recode a variable and its values are still missing. | 80% of missing data in a single variable
I think you have misunderstood the suggestion of the article: mainly because the suggestion makes no sense. You would then have two problems: how to recode a variable and its values are still missing. What was probably suggested was to create a missingness indicator.
A somewhat... | 80% of missing data in a single variable
I think you have misunderstood the suggestion of the article: mainly because the suggestion makes no sense. You would then have two problems: how to recode a variable and its values are still missing. |
15,468 | Examples of Simpson's Paradox being resolved by choosing the aggregate data | I can think of a topical example. If we look at cities overall, we see more coronavirus infections and deaths in denser cities. So clearly, density yields interactions yields infections yields deaths, yes?
Except this does not hold if we look inside cities. Inside cities, often the areas with higher density have fewer ... | Examples of Simpson's Paradox being resolved by choosing the aggregate data | I can think of a topical example. If we look at cities overall, we see more coronavirus infections and deaths in denser cities. So clearly, density yields interactions yields infections yields deaths, | Examples of Simpson's Paradox being resolved by choosing the aggregate data
I can think of a topical example. If we look at cities overall, we see more coronavirus infections and deaths in denser cities. So clearly, density yields interactions yields infections yields deaths, yes?
Except this does not hold if we look i... | Examples of Simpson's Paradox being resolved by choosing the aggregate data
I can think of a topical example. If we look at cities overall, we see more coronavirus infections and deaths in denser cities. So clearly, density yields interactions yields infections yields deaths, |
15,469 | Examples of Simpson's Paradox being resolved by choosing the aggregate data | It's going to be hard to find an example quite like that one, because of the number of groups and the fact that there is almost no unexplained variation.
A real, two-group one:
Smokers who have higher levels of vitamin A in their diet (or who have higher levels in their blood) have lower risk of developing lung cance... | Examples of Simpson's Paradox being resolved by choosing the aggregate data | It's going to be hard to find an example quite like that one, because of the number of groups and the fact that there is almost no unexplained variation.
A real, two-group one:
Smokers who have high | Examples of Simpson's Paradox being resolved by choosing the aggregate data
It's going to be hard to find an example quite like that one, because of the number of groups and the fact that there is almost no unexplained variation.
A real, two-group one:
Smokers who have higher levels of vitamin A in their diet (or who... | Examples of Simpson's Paradox being resolved by choosing the aggregate data
It's going to be hard to find an example quite like that one, because of the number of groups and the fact that there is almost no unexplained variation.
A real, two-group one:
Smokers who have high |
15,470 | Examples of Simpson's Paradox being resolved by choosing the aggregate data | TL/DR--it's just about covariates
Philosophical Introduction
"Simpson's paradox" is not really a "paradox" in the sense of the barber's paradox or others. It is more like some of Zeno's paradoxes of motion where the paradox results from either not using all of the available information, or not fully understanding the p... | Examples of Simpson's Paradox being resolved by choosing the aggregate data | TL/DR--it's just about covariates
Philosophical Introduction
"Simpson's paradox" is not really a "paradox" in the sense of the barber's paradox or others. It is more like some of Zeno's paradoxes of m | Examples of Simpson's Paradox being resolved by choosing the aggregate data
TL/DR--it's just about covariates
Philosophical Introduction
"Simpson's paradox" is not really a "paradox" in the sense of the barber's paradox or others. It is more like some of Zeno's paradoxes of motion where the paradox results from either ... | Examples of Simpson's Paradox being resolved by choosing the aggregate data
TL/DR--it's just about covariates
Philosophical Introduction
"Simpson's paradox" is not really a "paradox" in the sense of the barber's paradox or others. It is more like some of Zeno's paradoxes of m |
15,471 | Examples of Simpson's Paradox being resolved by choosing the aggregate data | I don't know of a real example, but maybe I can provide some helpful thoughts nonetheless.
The first thing is that the nature of "Simpson's paradox" has evolved over time. Today, it is widely known as the situation where there is a relationship between two variables (call them $X$ and $Y$) with a given direction, but ... | Examples of Simpson's Paradox being resolved by choosing the aggregate data | I don't know of a real example, but maybe I can provide some helpful thoughts nonetheless.
The first thing is that the nature of "Simpson's paradox" has evolved over time. Today, it is widely known a | Examples of Simpson's Paradox being resolved by choosing the aggregate data
I don't know of a real example, but maybe I can provide some helpful thoughts nonetheless.
The first thing is that the nature of "Simpson's paradox" has evolved over time. Today, it is widely known as the situation where there is a relationshi... | Examples of Simpson's Paradox being resolved by choosing the aggregate data
I don't know of a real example, but maybe I can provide some helpful thoughts nonetheless.
The first thing is that the nature of "Simpson's paradox" has evolved over time. Today, it is widely known a |
15,472 | LASSO and ridge from the Bayesian perspective: what about the tuning parameter? | Penalized regression estimators such as LASSO and ridge are said to correspond to Bayesian estimators with certain priors.
Yes, that is correct. Whenever we have an optimisation problem involving maximisation of the log-likelihood function plus a penalty function on the parameters, this is mathematically equivalent t... | LASSO and ridge from the Bayesian perspective: what about the tuning parameter? | Penalized regression estimators such as LASSO and ridge are said to correspond to Bayesian estimators with certain priors.
Yes, that is correct. Whenever we have an optimisation problem involving ma | LASSO and ridge from the Bayesian perspective: what about the tuning parameter?
Penalized regression estimators such as LASSO and ridge are said to correspond to Bayesian estimators with certain priors.
Yes, that is correct. Whenever we have an optimisation problem involving maximisation of the log-likelihood functio... | LASSO and ridge from the Bayesian perspective: what about the tuning parameter?
Penalized regression estimators such as LASSO and ridge are said to correspond to Bayesian estimators with certain priors.
Yes, that is correct. Whenever we have an optimisation problem involving ma |
15,473 | LASSO and ridge from the Bayesian perspective: what about the tuning parameter? | Indeed most penalized regression methods correspond to placing a particular type of prior to the regression coefficients. For example, you get the LASSO using a Laplace prior, and the ridge using a normal prior. The tuning parameters are the “hyperparameters” under the Bayesian formulation for which you can place an ad... | LASSO and ridge from the Bayesian perspective: what about the tuning parameter? | Indeed most penalized regression methods correspond to placing a particular type of prior to the regression coefficients. For example, you get the LASSO using a Laplace prior, and the ridge using a no | LASSO and ridge from the Bayesian perspective: what about the tuning parameter?
Indeed most penalized regression methods correspond to placing a particular type of prior to the regression coefficients. For example, you get the LASSO using a Laplace prior, and the ridge using a normal prior. The tuning parameters are th... | LASSO and ridge from the Bayesian perspective: what about the tuning parameter?
Indeed most penalized regression methods correspond to placing a particular type of prior to the regression coefficients. For example, you get the LASSO using a Laplace prior, and the ridge using a no |
15,474 | What's the relation between game theory and reinforcement learning? | In Reinforcement Learning (RL) it is common to imagine an underlying Markov Decision Process (MDP). Then the goal of RL is
to learn a good policy for the MDP, which is often only partially specified. MDPs can have different objectives such as total, average, or discounted reward, where discounted reward is the most co... | What's the relation between game theory and reinforcement learning? | In Reinforcement Learning (RL) it is common to imagine an underlying Markov Decision Process (MDP). Then the goal of RL is
to learn a good policy for the MDP, which is often only partially specified. | What's the relation between game theory and reinforcement learning?
In Reinforcement Learning (RL) it is common to imagine an underlying Markov Decision Process (MDP). Then the goal of RL is
to learn a good policy for the MDP, which is often only partially specified. MDPs can have different objectives such as total, a... | What's the relation between game theory and reinforcement learning?
In Reinforcement Learning (RL) it is common to imagine an underlying Markov Decision Process (MDP). Then the goal of RL is
to learn a good policy for the MDP, which is often only partially specified. |
15,475 | What's the relation between game theory and reinforcement learning? | Game theory is quite involved in the context of Multi-agent Reinforcement learning (MARL).
Take a look at stochastic games or read the article An Analysis of Stochastic Game Theory for Multiagent Reinforcement Learning.
I would not see GT as a prerequisite for RL. However, it provides a nice extension to the multi-age... | What's the relation between game theory and reinforcement learning? | Game theory is quite involved in the context of Multi-agent Reinforcement learning (MARL).
Take a look at stochastic games or read the article An Analysis of Stochastic Game Theory for Multiagent Rei | What's the relation between game theory and reinforcement learning?
Game theory is quite involved in the context of Multi-agent Reinforcement learning (MARL).
Take a look at stochastic games or read the article An Analysis of Stochastic Game Theory for Multiagent Reinforcement Learning.
I would not see GT as a prerequ... | What's the relation between game theory and reinforcement learning?
Game theory is quite involved in the context of Multi-agent Reinforcement learning (MARL).
Take a look at stochastic games or read the article An Analysis of Stochastic Game Theory for Multiagent Rei |
15,476 | What's the relation between game theory and reinforcement learning? | RL: A single agent is trained to solve a Markov decision problem (MDPS).
GT: Two agents are trained to solve Games. A multi-agent Reinforcement learning (MARL) can be used to solve for stochastic games.
If you are interested in the single-agent application of RL in deep learning, then you do not need to go for any GT c... | What's the relation between game theory and reinforcement learning? | RL: A single agent is trained to solve a Markov decision problem (MDPS).
GT: Two agents are trained to solve Games. A multi-agent Reinforcement learning (MARL) can be used to solve for stochastic game | What's the relation between game theory and reinforcement learning?
RL: A single agent is trained to solve a Markov decision problem (MDPS).
GT: Two agents are trained to solve Games. A multi-agent Reinforcement learning (MARL) can be used to solve for stochastic games.
If you are interested in the single-agent applica... | What's the relation between game theory and reinforcement learning?
RL: A single agent is trained to solve a Markov decision problem (MDPS).
GT: Two agents are trained to solve Games. A multi-agent Reinforcement learning (MARL) can be used to solve for stochastic game |
15,477 | What's the relation between game theory and reinforcement learning? | If you already know game theory you may see many parallels with reinforcement learning with multiple agents. Decision theory which is essentially game theory with one player is a second area that similar and perhaps a closer match for single agent settings.
Strict Domination and Backwards Induction solution concepts ma... | What's the relation between game theory and reinforcement learning? | If you already know game theory you may see many parallels with reinforcement learning with multiple agents. Decision theory which is essentially game theory with one player is a second area that simi | What's the relation between game theory and reinforcement learning?
If you already know game theory you may see many parallels with reinforcement learning with multiple agents. Decision theory which is essentially game theory with one player is a second area that similar and perhaps a closer match for single agent sett... | What's the relation between game theory and reinforcement learning?
If you already know game theory you may see many parallels with reinforcement learning with multiple agents. Decision theory which is essentially game theory with one player is a second area that simi |
15,478 | What's the relation between game theory and reinforcement learning? | RL environment can be modelled using MDP(Markov Decision Process), in case you are dealing with one single agent. If the environment consists of multiple agents, in this case it is called MultiAgent RL (MARL), then Game Theory (GT) may help. GT is used with MARL when there exists any sort of competition between the age... | What's the relation between game theory and reinforcement learning? | RL environment can be modelled using MDP(Markov Decision Process), in case you are dealing with one single agent. If the environment consists of multiple agents, in this case it is called MultiAgent R | What's the relation between game theory and reinforcement learning?
RL environment can be modelled using MDP(Markov Decision Process), in case you are dealing with one single agent. If the environment consists of multiple agents, in this case it is called MultiAgent RL (MARL), then Game Theory (GT) may help. GT is used... | What's the relation between game theory and reinforcement learning?
RL environment can be modelled using MDP(Markov Decision Process), in case you are dealing with one single agent. If the environment consists of multiple agents, in this case it is called MultiAgent R |
15,479 | why boosting method is sensitive to outliers | Outliers can be bad for boosting because boosting builds each tree on previous trees' residuals/errors. Outliers will have much larger residuals than non-outliers, so gradient boosting will focus a disproportionate amount of its attention on those points. | why boosting method is sensitive to outliers | Outliers can be bad for boosting because boosting builds each tree on previous trees' residuals/errors. Outliers will have much larger residuals than non-outliers, so gradient boosting will focus a di | why boosting method is sensitive to outliers
Outliers can be bad for boosting because boosting builds each tree on previous trees' residuals/errors. Outliers will have much larger residuals than non-outliers, so gradient boosting will focus a disproportionate amount of its attention on those points. | why boosting method is sensitive to outliers
Outliers can be bad for boosting because boosting builds each tree on previous trees' residuals/errors. Outliers will have much larger residuals than non-outliers, so gradient boosting will focus a di |
15,480 | why boosting method is sensitive to outliers | The algorithms you specified are for classification, so I'm assuming you don't mean outliers in the target variable, but input variable outliers. Boosted Tree methods should be fairly robust to outliers in the input features since the base learners are tree splits. For example, if the split is x > 3 then 5 and 5,000,... | why boosting method is sensitive to outliers | The algorithms you specified are for classification, so I'm assuming you don't mean outliers in the target variable, but input variable outliers. Boosted Tree methods should be fairly robust to outli | why boosting method is sensitive to outliers
The algorithms you specified are for classification, so I'm assuming you don't mean outliers in the target variable, but input variable outliers. Boosted Tree methods should be fairly robust to outliers in the input features since the base learners are tree splits. For exa... | why boosting method is sensitive to outliers
The algorithms you specified are for classification, so I'm assuming you don't mean outliers in the target variable, but input variable outliers. Boosted Tree methods should be fairly robust to outli |
15,481 | why boosting method is sensitive to outliers | A nice literature review of this topic can be found in
Alexander Hanbo Li and Jelena Bradic "Boosting in the presence of outliers: adaptive classification with non-convex loss functions" Journal of the American Statistical Association. 2018. Preprint link: https://arxiv.org/abs/1510.01064
Recent advances in technolog... | why boosting method is sensitive to outliers | A nice literature review of this topic can be found in
Alexander Hanbo Li and Jelena Bradic "Boosting in the presence of outliers: adaptive classification with non-convex loss functions" Journal of t | why boosting method is sensitive to outliers
A nice literature review of this topic can be found in
Alexander Hanbo Li and Jelena Bradic "Boosting in the presence of outliers: adaptive classification with non-convex loss functions" Journal of the American Statistical Association. 2018. Preprint link: https://arxiv.org... | why boosting method is sensitive to outliers
A nice literature review of this topic can be found in
Alexander Hanbo Li and Jelena Bradic "Boosting in the presence of outliers: adaptive classification with non-convex loss functions" Journal of t |
15,482 | why boosting method is sensitive to outliers | In boosting we try to pick the dataset on which the algorithm results were poor instead of randomly choosing the subset of data. These hard examples are important ones to learn, so if the data set has a lot of outliers and algorithm is not performing good on those ones than to learn those hard examples algorithm will t... | why boosting method is sensitive to outliers | In boosting we try to pick the dataset on which the algorithm results were poor instead of randomly choosing the subset of data. These hard examples are important ones to learn, so if the data set has | why boosting method is sensitive to outliers
In boosting we try to pick the dataset on which the algorithm results were poor instead of randomly choosing the subset of data. These hard examples are important ones to learn, so if the data set has a lot of outliers and algorithm is not performing good on those ones than ... | why boosting method is sensitive to outliers
In boosting we try to pick the dataset on which the algorithm results were poor instead of randomly choosing the subset of data. These hard examples are important ones to learn, so if the data set has |
15,483 | Use of nested cross-validation | Nested cross-validation is used to avoid optimistically biased estimates of performance that result from using the same cross-validation to set the values of the hyper-parameters of the model (e.g. the regularisation parameter, $C$, and kernel parameters of an SVM) and performance estimation. I wrote a paper on this t... | Use of nested cross-validation | Nested cross-validation is used to avoid optimistically biased estimates of performance that result from using the same cross-validation to set the values of the hyper-parameters of the model (e.g. th | Use of nested cross-validation
Nested cross-validation is used to avoid optimistically biased estimates of performance that result from using the same cross-validation to set the values of the hyper-parameters of the model (e.g. the regularisation parameter, $C$, and kernel parameters of an SVM) and performance estimat... | Use of nested cross-validation
Nested cross-validation is used to avoid optimistically biased estimates of performance that result from using the same cross-validation to set the values of the hyper-parameters of the model (e.g. th |
15,484 | Use of nested cross-validation | With a held-out test set clf.fit produces one unbiased estimate while nested cross-validation with cross_val_score produces several unbiased estimates. The advantage of nested cross-validation is a better assessment of the true performance using data that the algorithm hasn't seen yet. Better assessment because you get... | Use of nested cross-validation | With a held-out test set clf.fit produces one unbiased estimate while nested cross-validation with cross_val_score produces several unbiased estimates. The advantage of nested cross-validation is a be | Use of nested cross-validation
With a held-out test set clf.fit produces one unbiased estimate while nested cross-validation with cross_val_score produces several unbiased estimates. The advantage of nested cross-validation is a better assessment of the true performance using data that the algorithm hasn't seen yet. Be... | Use of nested cross-validation
With a held-out test set clf.fit produces one unbiased estimate while nested cross-validation with cross_val_score produces several unbiased estimates. The advantage of nested cross-validation is a be |
15,485 | When does Naive Bayes perform better than SVM? | There is no single answer about which is the best classification method for a given dataset. Different kinds of classifiers should be always considered for a comparative study over a given dataset. Given the properties of the dataset, you might have some clues that may give preference to some methods. However, it would... | When does Naive Bayes perform better than SVM? | There is no single answer about which is the best classification method for a given dataset. Different kinds of classifiers should be always considered for a comparative study over a given dataset. Gi | When does Naive Bayes perform better than SVM?
There is no single answer about which is the best classification method for a given dataset. Different kinds of classifiers should be always considered for a comparative study over a given dataset. Given the properties of the dataset, you might have some clues that may giv... | When does Naive Bayes perform better than SVM?
There is no single answer about which is the best classification method for a given dataset. Different kinds of classifiers should be always considered for a comparative study over a given dataset. Gi |
15,486 | What exactly does it mean to 'pool data'? | Yes, your examples are correct.
The Oxford English Dictionary defines pool as:
pool, v.
(puːl)
1.1 trans. To throw into a common stock or fund to be distributed according to agreement; to combine (capital or interests) for the common benefit; spec. of competing railway companies, etc.: To share or divide (traffic or ... | What exactly does it mean to 'pool data'? | Yes, your examples are correct.
The Oxford English Dictionary defines pool as:
pool, v.
(puːl)
1.1 trans. To throw into a common stock or fund to be distributed according to agreement; to combine (c | What exactly does it mean to 'pool data'?
Yes, your examples are correct.
The Oxford English Dictionary defines pool as:
pool, v.
(puːl)
1.1 trans. To throw into a common stock or fund to be distributed according to agreement; to combine (capital or interests) for the common benefit; spec. of competing railway compan... | What exactly does it mean to 'pool data'?
Yes, your examples are correct.
The Oxford English Dictionary defines pool as:
pool, v.
(puːl)
1.1 trans. To throw into a common stock or fund to be distributed according to agreement; to combine (c |
15,487 | What exactly does it mean to 'pool data'? | Pooling can refer to combining data, but it can also refer to combining information rather than the raw data. One of the most common uses of pooling is in estimating a variance. If we believe that 2 populations have the same variance, but not necesarily the same mean, then we can calculate the 2 estimates of the vari... | What exactly does it mean to 'pool data'? | Pooling can refer to combining data, but it can also refer to combining information rather than the raw data. One of the most common uses of pooling is in estimating a variance. If we believe that 2 | What exactly does it mean to 'pool data'?
Pooling can refer to combining data, but it can also refer to combining information rather than the raw data. One of the most common uses of pooling is in estimating a variance. If we believe that 2 populations have the same variance, but not necesarily the same mean, then we... | What exactly does it mean to 'pool data'?
Pooling can refer to combining data, but it can also refer to combining information rather than the raw data. One of the most common uses of pooling is in estimating a variance. If we believe that 2 |
15,488 | Quality assurance and quality control (QA/QC) guidelines for a database | This response focuses on the second question, but in the process a partial answer to the first question (guidelines for a QA/QC procedure) will emerge.
By far the best thing you can do is check data quality at the time entry is attempted. The user checks and reports are labor-intensive and so should be reserved for la... | Quality assurance and quality control (QA/QC) guidelines for a database | This response focuses on the second question, but in the process a partial answer to the first question (guidelines for a QA/QC procedure) will emerge.
By far the best thing you can do is check data q | Quality assurance and quality control (QA/QC) guidelines for a database
This response focuses on the second question, but in the process a partial answer to the first question (guidelines for a QA/QC procedure) will emerge.
By far the best thing you can do is check data quality at the time entry is attempted. The user... | Quality assurance and quality control (QA/QC) guidelines for a database
This response focuses on the second question, but in the process a partial answer to the first question (guidelines for a QA/QC procedure) will emerge.
By far the best thing you can do is check data q |
15,489 | Quality assurance and quality control (QA/QC) guidelines for a database | DataOne provides a helpful set of data management best practices that can be filtered by tag. The best practices tagged with "quality", found at http://www.dataone.org/best-practices/quality, reiterating and expanding on many of the points made by @whuber. Here is a list of the topics covered there (in alphabetical ord... | Quality assurance and quality control (QA/QC) guidelines for a database | DataOne provides a helpful set of data management best practices that can be filtered by tag. The best practices tagged with "quality", found at http://www.dataone.org/best-practices/quality, reiterat | Quality assurance and quality control (QA/QC) guidelines for a database
DataOne provides a helpful set of data management best practices that can be filtered by tag. The best practices tagged with "quality", found at http://www.dataone.org/best-practices/quality, reiterating and expanding on many of the points made by ... | Quality assurance and quality control (QA/QC) guidelines for a database
DataOne provides a helpful set of data management best practices that can be filtered by tag. The best practices tagged with "quality", found at http://www.dataone.org/best-practices/quality, reiterat |
15,490 | Essential papers on matrix decompositions | How do you know that SVD and NMF are by far the most used matrix decompositions rather than LU, Cholesky and QR? My personal favourite 'breakthrough' would have to be the guaranteed rank-revealing QR algorithm,
Chan, Tony F. "Rank revealing QR factorizations". Linear Algebra and its Applications Volumes 88-89, April 1... | Essential papers on matrix decompositions | How do you know that SVD and NMF are by far the most used matrix decompositions rather than LU, Cholesky and QR? My personal favourite 'breakthrough' would have to be the guaranteed rank-revealing QR | Essential papers on matrix decompositions
How do you know that SVD and NMF are by far the most used matrix decompositions rather than LU, Cholesky and QR? My personal favourite 'breakthrough' would have to be the guaranteed rank-revealing QR algorithm,
Chan, Tony F. "Rank revealing QR factorizations". Linear Algebra a... | Essential papers on matrix decompositions
How do you know that SVD and NMF are by far the most used matrix decompositions rather than LU, Cholesky and QR? My personal favourite 'breakthrough' would have to be the guaranteed rank-revealing QR |
15,491 | Essential papers on matrix decompositions | For NNMF, Lee and Seung describe an iterative algorithm which is very simple to implement. Actually they give two similar algorithms, one for minimizing Frobenius norm of residual, the other for minimizing Kullback-Leibler Divergence of the approximation and original matrix.
Daniel Lee, H. Sebastian Seung, Algorithms ... | Essential papers on matrix decompositions | For NNMF, Lee and Seung describe an iterative algorithm which is very simple to implement. Actually they give two similar algorithms, one for minimizing Frobenius norm of residual, the other for minim | Essential papers on matrix decompositions
For NNMF, Lee and Seung describe an iterative algorithm which is very simple to implement. Actually they give two similar algorithms, one for minimizing Frobenius norm of residual, the other for minimizing Kullback-Leibler Divergence of the approximation and original matrix.
D... | Essential papers on matrix decompositions
For NNMF, Lee and Seung describe an iterative algorithm which is very simple to implement. Actually they give two similar algorithms, one for minimizing Frobenius norm of residual, the other for minim |
15,492 | Essential papers on matrix decompositions | Maybe, you can find interesting
[Learning with Matrix Factorizations] PhD thesis by Nathan Srebro,
[Investigation of Various Matrix Factorization Methods for Large Recommender Systems], Gábor Takács et.al. and almost the same technique described here
The last two links show how sparse matrix factorizations are used... | Essential papers on matrix decompositions | Maybe, you can find interesting
[Learning with Matrix Factorizations] PhD thesis by Nathan Srebro,
[Investigation of Various Matrix Factorization Methods for Large Recommender Systems], Gábor Takács | Essential papers on matrix decompositions
Maybe, you can find interesting
[Learning with Matrix Factorizations] PhD thesis by Nathan Srebro,
[Investigation of Various Matrix Factorization Methods for Large Recommender Systems], Gábor Takács et.al. and almost the same technique described here
The last two links show... | Essential papers on matrix decompositions
Maybe, you can find interesting
[Learning with Matrix Factorizations] PhD thesis by Nathan Srebro,
[Investigation of Various Matrix Factorization Methods for Large Recommender Systems], Gábor Takács |
15,493 | Essential papers on matrix decompositions | Witten, Tibshirani - Penalized matrix decomposition
http://www.biostat.washington.edu/~dwitten/Papers/pmd.pdf
http://cran.r-project.org/web/packages/PMA/index.html
Martinsson, Rokhlin, Szlam, Tygert - Randomized SVD
http://cims.nyu.edu/~tygert/software.html
http://cims.nyu.edu/~tygert/blanczos.pdf | Essential papers on matrix decompositions | Witten, Tibshirani - Penalized matrix decomposition
http://www.biostat.washington.edu/~dwitten/Papers/pmd.pdf
http://cran.r-project.org/web/packages/PMA/index.html
Martinsson, Rokhlin, Szlam, Tygert - | Essential papers on matrix decompositions
Witten, Tibshirani - Penalized matrix decomposition
http://www.biostat.washington.edu/~dwitten/Papers/pmd.pdf
http://cran.r-project.org/web/packages/PMA/index.html
Martinsson, Rokhlin, Szlam, Tygert - Randomized SVD
http://cims.nyu.edu/~tygert/software.html
http://cims.nyu.edu/... | Essential papers on matrix decompositions
Witten, Tibshirani - Penalized matrix decomposition
http://www.biostat.washington.edu/~dwitten/Papers/pmd.pdf
http://cran.r-project.org/web/packages/PMA/index.html
Martinsson, Rokhlin, Szlam, Tygert - |
15,494 | Essential papers on matrix decompositions | At this year's NIPS there was a short paper on distributed, very large-scale SVD that works in a single pass over a streaming input matrix.
The paper's more implementation-oriented but puts things into perspective with real wall-clock times and all. The table near the beginning is a good survey too. | Essential papers on matrix decompositions | At this year's NIPS there was a short paper on distributed, very large-scale SVD that works in a single pass over a streaming input matrix.
The paper's more implementation-oriented but puts things int | Essential papers on matrix decompositions
At this year's NIPS there was a short paper on distributed, very large-scale SVD that works in a single pass over a streaming input matrix.
The paper's more implementation-oriented but puts things into perspective with real wall-clock times and all. The table near the beginning... | Essential papers on matrix decompositions
At this year's NIPS there was a short paper on distributed, very large-scale SVD that works in a single pass over a streaming input matrix.
The paper's more implementation-oriented but puts things int |
15,495 | What are the pros and cons of using the logrank vs. the Mantel-Haenszel method for computing the Hazard Ratio in survival analysis? | I think I figured out the answer (to my own question). If the assumption of proportional hazards is true, the two methods give similar estimates of the hazard ratio. The discrepancy I found in one particular example, I now think, is due to the fact that that assumption is dubious.
If the assumption of proportional haz... | What are the pros and cons of using the logrank vs. the Mantel-Haenszel method for computing the Haz | I think I figured out the answer (to my own question). If the assumption of proportional hazards is true, the two methods give similar estimates of the hazard ratio. The discrepancy I found in one par | What are the pros and cons of using the logrank vs. the Mantel-Haenszel method for computing the Hazard Ratio in survival analysis?
I think I figured out the answer (to my own question). If the assumption of proportional hazards is true, the two methods give similar estimates of the hazard ratio. The discrepancy I foun... | What are the pros and cons of using the logrank vs. the Mantel-Haenszel method for computing the Haz
I think I figured out the answer (to my own question). If the assumption of proportional hazards is true, the two methods give similar estimates of the hazard ratio. The discrepancy I found in one par |
15,496 | What are the pros and cons of using the logrank vs. the Mantel-Haenszel method for computing the Hazard Ratio in survival analysis? | If I'm not mistaken, the log-rank estimator you reference is also known as the Pike estimator. I believe it's generally recommended for HR < 3 because it exhibits less bias in that range. The following paper may be of interest (note that the paper refers to it as O/E):
Estimation of the Proportional Hazard in Two-Tr... | What are the pros and cons of using the logrank vs. the Mantel-Haenszel method for computing the Haz | If I'm not mistaken, the log-rank estimator you reference is also known as the Pike estimator. I believe it's generally recommended for HR < 3 because it exhibits less bias in that range. The follow | What are the pros and cons of using the logrank vs. the Mantel-Haenszel method for computing the Hazard Ratio in survival analysis?
If I'm not mistaken, the log-rank estimator you reference is also known as the Pike estimator. I believe it's generally recommended for HR < 3 because it exhibits less bias in that range.... | What are the pros and cons of using the logrank vs. the Mantel-Haenszel method for computing the Haz
If I'm not mistaken, the log-rank estimator you reference is also known as the Pike estimator. I believe it's generally recommended for HR < 3 because it exhibits less bias in that range. The follow |
15,497 | What are the pros and cons of using the logrank vs. the Mantel-Haenszel method for computing the Hazard Ratio in survival analysis? | There are actually several more methods and the choice often depends on whether you are most interested in looking for early differences, later differences or - as for the log-rank test & the Mantel-Haenszel test - give equal weight to all time points.
To the question at hand. The log-rank test is in fact a form of the... | What are the pros and cons of using the logrank vs. the Mantel-Haenszel method for computing the Haz | There are actually several more methods and the choice often depends on whether you are most interested in looking for early differences, later differences or - as for the log-rank test & the Mantel-H | What are the pros and cons of using the logrank vs. the Mantel-Haenszel method for computing the Hazard Ratio in survival analysis?
There are actually several more methods and the choice often depends on whether you are most interested in looking for early differences, later differences or - as for the log-rank test & ... | What are the pros and cons of using the logrank vs. the Mantel-Haenszel method for computing the Haz
There are actually several more methods and the choice often depends on whether you are most interested in looking for early differences, later differences or - as for the log-rank test & the Mantel-H |
15,498 | What are the pros and cons of using the logrank vs. the Mantel-Haenszel method for computing the Hazard Ratio in survival analysis? | I thought I'd stumbled across a web site and reference that deals exactly with this question:
http://www.graphpad.com/faq/viewfaq.cfm?faq=1226
Start from "The two methods compared".
The site references the Berstein paper ars linked (above):
http://www.jstor.org/stable/2530564?seq=1
The site summarises Berstein et al's ... | What are the pros and cons of using the logrank vs. the Mantel-Haenszel method for computing the Haz | I thought I'd stumbled across a web site and reference that deals exactly with this question:
http://www.graphpad.com/faq/viewfaq.cfm?faq=1226
Start from "The two methods compared".
The site reference | What are the pros and cons of using the logrank vs. the Mantel-Haenszel method for computing the Hazard Ratio in survival analysis?
I thought I'd stumbled across a web site and reference that deals exactly with this question:
http://www.graphpad.com/faq/viewfaq.cfm?faq=1226
Start from "The two methods compared".
The si... | What are the pros and cons of using the logrank vs. the Mantel-Haenszel method for computing the Haz
I thought I'd stumbled across a web site and reference that deals exactly with this question:
http://www.graphpad.com/faq/viewfaq.cfm?faq=1226
Start from "The two methods compared".
The site reference |
15,499 | What is the problem with empirical priors? | Generally, informative priors are typically viewed as your information about parameters (or hypotheses) before seeing the data. So any data-based prior is violating the likelihood principle since evidence from the sample is coming through the likelihood function and the prior. | What is the problem with empirical priors? | Generally, informative priors are typically viewed as your information about parameters (or hypotheses) before seeing the data. So any data-based prior is violating the likelihood principle since evid | What is the problem with empirical priors?
Generally, informative priors are typically viewed as your information about parameters (or hypotheses) before seeing the data. So any data-based prior is violating the likelihood principle since evidence from the sample is coming through the likelihood function and the prior. | What is the problem with empirical priors?
Generally, informative priors are typically viewed as your information about parameters (or hypotheses) before seeing the data. So any data-based prior is violating the likelihood principle since evid |
15,500 | What is the problem with empirical priors? | The $p$-values are wrong. Take a simple example. Test whether a population mean $\mu$ is equal to a particular value $\mu_0$ or not. Suppose the sample mean $\bar x$ is greater than $\mu_0$. Then it would be simply wrong to let the data guide you into testing only a one-sided alternative. Your $p$-value will be half of... | What is the problem with empirical priors? | The $p$-values are wrong. Take a simple example. Test whether a population mean $\mu$ is equal to a particular value $\mu_0$ or not. Suppose the sample mean $\bar x$ is greater than $\mu_0$. Then it w | What is the problem with empirical priors?
The $p$-values are wrong. Take a simple example. Test whether a population mean $\mu$ is equal to a particular value $\mu_0$ or not. Suppose the sample mean $\bar x$ is greater than $\mu_0$. Then it would be simply wrong to let the data guide you into testing only a one-sided ... | What is the problem with empirical priors?
The $p$-values are wrong. Take a simple example. Test whether a population mean $\mu$ is equal to a particular value $\mu_0$ or not. Suppose the sample mean $\bar x$ is greater than $\mu_0$. Then it w |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.