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 |
|---|---|---|---|---|---|---|
6,801 | The meaning of "positive dependency" as a condition to use the usual method for FDR control | Positive dependence in this case means that the set of tests are positively correlated. The idea then is that if the variables in the set of tests that you have P-values for are positively correlated then each of the variables are not independent.
If you think back about a Bonferroni p-value correction, for example, y... | The meaning of "positive dependency" as a condition to use the usual method for FDR control | Positive dependence in this case means that the set of tests are positively correlated. The idea then is that if the variables in the set of tests that you have P-values for are positively correlated | The meaning of "positive dependency" as a condition to use the usual method for FDR control
Positive dependence in this case means that the set of tests are positively correlated. The idea then is that if the variables in the set of tests that you have P-values for are positively correlated then each of the variables a... | The meaning of "positive dependency" as a condition to use the usual method for FDR control
Positive dependence in this case means that the set of tests are positively correlated. The idea then is that if the variables in the set of tests that you have P-values for are positively correlated |
6,802 | Pooling vs. stride for downsampling | The advantage of the convolution layer is that it can learn certain properties that you might not think of while you add pooling layer. Pooling is a fixed operation and convolution can be learned. On the other hand, pooling is a cheaper operation than convolution, both in terms of the amount of computation that you nee... | Pooling vs. stride for downsampling | The advantage of the convolution layer is that it can learn certain properties that you might not think of while you add pooling layer. Pooling is a fixed operation and convolution can be learned. On | Pooling vs. stride for downsampling
The advantage of the convolution layer is that it can learn certain properties that you might not think of while you add pooling layer. Pooling is a fixed operation and convolution can be learned. On the other hand, pooling is a cheaper operation than convolution, both in terms of th... | Pooling vs. stride for downsampling
The advantage of the convolution layer is that it can learn certain properties that you might not think of while you add pooling layer. Pooling is a fixed operation and convolution can be learned. On |
6,803 | Pooling vs. stride for downsampling | In essence, max-pooling (or any kind of pooling) is a fixed operation and replacing it with a strided convolution can also be seen as learning the pooling operation, which increases the model's expressiveness ability. The down side is that it also increases the number of trainable parameters, but this is not a real pro... | Pooling vs. stride for downsampling | In essence, max-pooling (or any kind of pooling) is a fixed operation and replacing it with a strided convolution can also be seen as learning the pooling operation, which increases the model's expres | Pooling vs. stride for downsampling
In essence, max-pooling (or any kind of pooling) is a fixed operation and replacing it with a strided convolution can also be seen as learning the pooling operation, which increases the model's expressiveness ability. The down side is that it also increases the number of trainable pa... | Pooling vs. stride for downsampling
In essence, max-pooling (or any kind of pooling) is a fixed operation and replacing it with a strided convolution can also be seen as learning the pooling operation, which increases the model's expres |
6,804 | Is there Factor analysis or PCA for ordinal or binary data? | Traditional (linear) PCA and Factor analysis require scale-level (interval or ratio) data. Often likert-type rating data are assumed to be scale-level, because such data are easier to analyze. And the decision is sometimes warranted statistically, especially when the number of ordered categories is greater than 5 or 6.... | Is there Factor analysis or PCA for ordinal or binary data? | Traditional (linear) PCA and Factor analysis require scale-level (interval or ratio) data. Often likert-type rating data are assumed to be scale-level, because such data are easier to analyze. And the | Is there Factor analysis or PCA for ordinal or binary data?
Traditional (linear) PCA and Factor analysis require scale-level (interval or ratio) data. Often likert-type rating data are assumed to be scale-level, because such data are easier to analyze. And the decision is sometimes warranted statistically, especially w... | Is there Factor analysis or PCA for ordinal or binary data?
Traditional (linear) PCA and Factor analysis require scale-level (interval or ratio) data. Often likert-type rating data are assumed to be scale-level, because such data are easier to analyze. And the |
6,805 | Difference between Bayesian networks and Markov process? | A probabilistic graphical model (PGM) is a graph formalism for compactly modeling joint probability distributions and (in)dependence relations over a set of random variables. A PGM is called a Bayesian network when the underlying graph is directed, and a Markov network/Markov random field when the underlying graph is u... | Difference between Bayesian networks and Markov process? | A probabilistic graphical model (PGM) is a graph formalism for compactly modeling joint probability distributions and (in)dependence relations over a set of random variables. A PGM is called a Bayesia | Difference between Bayesian networks and Markov process?
A probabilistic graphical model (PGM) is a graph formalism for compactly modeling joint probability distributions and (in)dependence relations over a set of random variables. A PGM is called a Bayesian network when the underlying graph is directed, and a Markov n... | Difference between Bayesian networks and Markov process?
A probabilistic graphical model (PGM) is a graph formalism for compactly modeling joint probability distributions and (in)dependence relations over a set of random variables. A PGM is called a Bayesia |
6,806 | Difference between Bayesian networks and Markov process? | First a few words about Markov Processes. There are four distinct flavours of that beast, depending on the state space (discrete/continuous) and time variable (discrete/ continuous). The general idea of any Markov Process is that "given the present, future is independent of the past".
The simplest Markov Process, is d... | Difference between Bayesian networks and Markov process? | First a few words about Markov Processes. There are four distinct flavours of that beast, depending on the state space (discrete/continuous) and time variable (discrete/ continuous). The general idea | Difference between Bayesian networks and Markov process?
First a few words about Markov Processes. There are four distinct flavours of that beast, depending on the state space (discrete/continuous) and time variable (discrete/ continuous). The general idea of any Markov Process is that "given the present, future is ind... | Difference between Bayesian networks and Markov process?
First a few words about Markov Processes. There are four distinct flavours of that beast, depending on the state space (discrete/continuous) and time variable (discrete/ continuous). The general idea |
6,807 | Difference between Bayesian networks and Markov process? | While I was searching for an answer to the same question I came across these answers. But none of them clarify the topic. When I found some good explanations I wanted to share with people who thought like me.
In book "Probabilistic reasoning in intelligent systems:Networks of Plausible Inference" written by Judea Pearl... | Difference between Bayesian networks and Markov process? | While I was searching for an answer to the same question I came across these answers. But none of them clarify the topic. When I found some good explanations I wanted to share with people who thought | Difference between Bayesian networks and Markov process?
While I was searching for an answer to the same question I came across these answers. But none of them clarify the topic. When I found some good explanations I wanted to share with people who thought like me.
In book "Probabilistic reasoning in intelligent system... | Difference between Bayesian networks and Markov process?
While I was searching for an answer to the same question I came across these answers. But none of them clarify the topic. When I found some good explanations I wanted to share with people who thought |
6,808 | Difference between Bayesian networks and Markov process? | A Markov process is a stochastic process with the Markovian property (when the index is the time, the Markovian property is a special conditional independence, which says given present, past and future are independent.)
A Bayesian network is a directed graphical model.
(A Markov random field is a undirected graphical m... | Difference between Bayesian networks and Markov process? | A Markov process is a stochastic process with the Markovian property (when the index is the time, the Markovian property is a special conditional independence, which says given present, past and futur | Difference between Bayesian networks and Markov process?
A Markov process is a stochastic process with the Markovian property (when the index is the time, the Markovian property is a special conditional independence, which says given present, past and future are independent.)
A Bayesian network is a directed graphical ... | Difference between Bayesian networks and Markov process?
A Markov process is a stochastic process with the Markovian property (when the index is the time, the Markovian property is a special conditional independence, which says given present, past and futur |
6,809 | Difference between Bayesian networks and Markov process? | -The general idea of any Markov Process is that "given the present, future is independent of the past".
-The general idea of any Bayesian method is that "given the prior, future is independent of the past", its parameters, if indexed by observations, will follow a Markov process
PLUS
"all the following will be the sam... | Difference between Bayesian networks and Markov process? | -The general idea of any Markov Process is that "given the present, future is independent of the past".
-The general idea of any Bayesian method is that "given the prior, future is independent of the | Difference between Bayesian networks and Markov process?
-The general idea of any Markov Process is that "given the present, future is independent of the past".
-The general idea of any Bayesian method is that "given the prior, future is independent of the past", its parameters, if indexed by observations, will follow ... | Difference between Bayesian networks and Markov process?
-The general idea of any Markov Process is that "given the present, future is independent of the past".
-The general idea of any Bayesian method is that "given the prior, future is independent of the |
6,810 | Why do we need to normalize the images before we put them into CNN? | First note: you really should be also dividing by the standard deviation of each feature (pixel) value as well. Subtracting the mean centers the input to 0, and dividing by the standard deviation makes any scaled feature value the number of standard deviations away from the mean.
To answer your question: Consider how ... | Why do we need to normalize the images before we put them into CNN? | First note: you really should be also dividing by the standard deviation of each feature (pixel) value as well. Subtracting the mean centers the input to 0, and dividing by the standard deviation make | Why do we need to normalize the images before we put them into CNN?
First note: you really should be also dividing by the standard deviation of each feature (pixel) value as well. Subtracting the mean centers the input to 0, and dividing by the standard deviation makes any scaled feature value the number of standard de... | Why do we need to normalize the images before we put them into CNN?
First note: you really should be also dividing by the standard deviation of each feature (pixel) value as well. Subtracting the mean centers the input to 0, and dividing by the standard deviation make |
6,811 | Is it possible to find the combined standard deviation? | So, if you just want to have two of these samples brought together into one you have:
$s_1 = \sqrt{\frac{1}{n_1}\Sigma_{i = 1}^{n_1} (x_i - \bar{y}_1)^2}$
$s_2 = \sqrt{\frac{1}{n_2}\Sigma_{i = 1}^{n_2} (y_i - \bar{y}_2)^2}$
where $\bar{y}_1$ and $\bar{y}_2$ are sample means and $s_1$ and $s_2$ are sample standard devia... | Is it possible to find the combined standard deviation? | So, if you just want to have two of these samples brought together into one you have:
$s_1 = \sqrt{\frac{1}{n_1}\Sigma_{i = 1}^{n_1} (x_i - \bar{y}_1)^2}$
$s_2 = \sqrt{\frac{1}{n_2}\Sigma_{i = 1}^{n_2 | Is it possible to find the combined standard deviation?
So, if you just want to have two of these samples brought together into one you have:
$s_1 = \sqrt{\frac{1}{n_1}\Sigma_{i = 1}^{n_1} (x_i - \bar{y}_1)^2}$
$s_2 = \sqrt{\frac{1}{n_2}\Sigma_{i = 1}^{n_2} (y_i - \bar{y}_2)^2}$
where $\bar{y}_1$ and $\bar{y}_2$ are sa... | Is it possible to find the combined standard deviation?
So, if you just want to have two of these samples brought together into one you have:
$s_1 = \sqrt{\frac{1}{n_1}\Sigma_{i = 1}^{n_1} (x_i - \bar{y}_1)^2}$
$s_2 = \sqrt{\frac{1}{n_2}\Sigma_{i = 1}^{n_2 |
6,812 | Is it possible to find the combined standard deviation? | This obviously extends to $K$ groups:
$$ s = \sqrt{ \frac{\sum_{k=1}^K (n_k-1)s_k^2 + n_k(\bar{y}_k-\bar{y})^2} {(\sum_{k=1}^K n_k) -1} }$$ | Is it possible to find the combined standard deviation? | This obviously extends to $K$ groups:
$$ s = \sqrt{ \frac{\sum_{k=1}^K (n_k-1)s_k^2 + n_k(\bar{y}_k-\bar{y})^2} {(\sum_{k=1}^K n_k) -1} }$$ | Is it possible to find the combined standard deviation?
This obviously extends to $K$ groups:
$$ s = \sqrt{ \frac{\sum_{k=1}^K (n_k-1)s_k^2 + n_k(\bar{y}_k-\bar{y})^2} {(\sum_{k=1}^K n_k) -1} }$$ | Is it possible to find the combined standard deviation?
This obviously extends to $K$ groups:
$$ s = \sqrt{ \frac{\sum_{k=1}^K (n_k-1)s_k^2 + n_k(\bar{y}_k-\bar{y})^2} {(\sum_{k=1}^K n_k) -1} }$$ |
6,813 | Is it possible to find the combined standard deviation? | I had the same problem: having the standard deviation, means and sizes of several subsets with empty intersection, compute the standard deviation of the union of those subsets.
I like the answer of sashkello and Glen_b ♦, but I wanted to find a proof of it. I did it in this way, and I leave it here in case it is of hel... | Is it possible to find the combined standard deviation? | I had the same problem: having the standard deviation, means and sizes of several subsets with empty intersection, compute the standard deviation of the union of those subsets.
I like the answer of sa | Is it possible to find the combined standard deviation?
I had the same problem: having the standard deviation, means and sizes of several subsets with empty intersection, compute the standard deviation of the union of those subsets.
I like the answer of sashkello and Glen_b ♦, but I wanted to find a proof of it. I did ... | Is it possible to find the combined standard deviation?
I had the same problem: having the standard deviation, means and sizes of several subsets with empty intersection, compute the standard deviation of the union of those subsets.
I like the answer of sa |
6,814 | How to calculate goodness of fit in glm (R) | Use the Null Deviance and the Residual Deviance, specifically:
1 - (Residual Deviance/Null Deviance)
If you think about it, you're trying to measure the ratio of the deviance in your model to the null; how much better your model is (residual deviance) than just the intercept (null deviance). If that ratio is tiny, you... | How to calculate goodness of fit in glm (R) | Use the Null Deviance and the Residual Deviance, specifically:
1 - (Residual Deviance/Null Deviance)
If you think about it, you're trying to measure the ratio of the deviance in your model to the nul | How to calculate goodness of fit in glm (R)
Use the Null Deviance and the Residual Deviance, specifically:
1 - (Residual Deviance/Null Deviance)
If you think about it, you're trying to measure the ratio of the deviance in your model to the null; how much better your model is (residual deviance) than just the intercept... | How to calculate goodness of fit in glm (R)
Use the Null Deviance and the Residual Deviance, specifically:
1 - (Residual Deviance/Null Deviance)
If you think about it, you're trying to measure the ratio of the deviance in your model to the nul |
6,815 | How to calculate goodness of fit in glm (R) | The default error family for a glm model in (the language) R is Gaussian, so with the code submitted you are getting ordinary linear regression where $R^2$ is a widely accepted measure of "goodness of fit". The R glm function doesn't report the Nagelkerke-pseudo-"$R^2$" but rather the AIC (Akaike Information Criterion)... | How to calculate goodness of fit in glm (R) | The default error family for a glm model in (the language) R is Gaussian, so with the code submitted you are getting ordinary linear regression where $R^2$ is a widely accepted measure of "goodness of | How to calculate goodness of fit in glm (R)
The default error family for a glm model in (the language) R is Gaussian, so with the code submitted you are getting ordinary linear regression where $R^2$ is a widely accepted measure of "goodness of fit". The R glm function doesn't report the Nagelkerke-pseudo-"$R^2$" but r... | How to calculate goodness of fit in glm (R)
The default error family for a glm model in (the language) R is Gaussian, so with the code submitted you are getting ordinary linear regression where $R^2$ is a widely accepted measure of "goodness of |
6,816 | How to calculate goodness of fit in glm (R) | If you are running a binary logistic model, you can also run the Hosmer Lemeshow Goodness of Fit test on your glm() model. Using the ResourceSelection library.
library(ResourceSelection)
model <- glm(tmpData$Y ~ tmpData$X1 + tmpData$X2 + tmpData$X3 +
as.numeric(tmpData$X4) + tmpData$X5 + tmpData$X6 + tmpDa... | How to calculate goodness of fit in glm (R) | If you are running a binary logistic model, you can also run the Hosmer Lemeshow Goodness of Fit test on your glm() model. Using the ResourceSelection library.
library(ResourceSelection)
model <- glm | How to calculate goodness of fit in glm (R)
If you are running a binary logistic model, you can also run the Hosmer Lemeshow Goodness of Fit test on your glm() model. Using the ResourceSelection library.
library(ResourceSelection)
model <- glm(tmpData$Y ~ tmpData$X1 + tmpData$X2 + tmpData$X3 +
as.numeric(t... | How to calculate goodness of fit in glm (R)
If you are running a binary logistic model, you can also run the Hosmer Lemeshow Goodness of Fit test on your glm() model. Using the ResourceSelection library.
library(ResourceSelection)
model <- glm |
6,817 | Why is lambda "within one standard error from the minimum" is a recommended value for lambda in an elastic net regression? | Friedman, Hastie, and Tibshirani (2010), citing The Elements of Statistical Learning, write,
We often use the “one-standard-error” rule when selecting the best model; this acknowledges the fact that the risk curves are estimated with error, so errs on the side of parsimony.
The reason for using one standard error, as... | Why is lambda "within one standard error from the minimum" is a recommended value for lambda in an e | Friedman, Hastie, and Tibshirani (2010), citing The Elements of Statistical Learning, write,
We often use the “one-standard-error” rule when selecting the best model; this acknowledges the fact that | Why is lambda "within one standard error from the minimum" is a recommended value for lambda in an elastic net regression?
Friedman, Hastie, and Tibshirani (2010), citing The Elements of Statistical Learning, write,
We often use the “one-standard-error” rule when selecting the best model; this acknowledges the fact th... | Why is lambda "within one standard error from the minimum" is a recommended value for lambda in an e
Friedman, Hastie, and Tibshirani (2010), citing The Elements of Statistical Learning, write,
We often use the “one-standard-error” rule when selecting the best model; this acknowledges the fact that |
6,818 | Why is lambda "within one standard error from the minimum" is a recommended value for lambda in an elastic net regression? | Breiman et al.'s book (cited in the other answer's quote from Krstajic) is the oldest reference I've found for the 1SE rule.
This is Breiman, Friedman, Stone, and Olshen's Classification and Regression Trees (1984). They "derive" this rule in section 3.4.3.
So if you need a formal citation, that seems to be the origina... | Why is lambda "within one standard error from the minimum" is a recommended value for lambda in an e | Breiman et al.'s book (cited in the other answer's quote from Krstajic) is the oldest reference I've found for the 1SE rule.
This is Breiman, Friedman, Stone, and Olshen's Classification and Regressio | Why is lambda "within one standard error from the minimum" is a recommended value for lambda in an elastic net regression?
Breiman et al.'s book (cited in the other answer's quote from Krstajic) is the oldest reference I've found for the 1SE rule.
This is Breiman, Friedman, Stone, and Olshen's Classification and Regres... | Why is lambda "within one standard error from the minimum" is a recommended value for lambda in an e
Breiman et al.'s book (cited in the other answer's quote from Krstajic) is the oldest reference I've found for the 1SE rule.
This is Breiman, Friedman, Stone, and Olshen's Classification and Regressio |
6,819 | Graphical data overview (summary) function in R | Frank Harrell's Hmisc package has some basic graphics with options for annotation: check out the summary.formula() and related plot wrap functions. I also like the describe() function.
For additional information, have a look at the The Hmisc Library or An Introduction to S-Plus and the Hmisc and Design Libraries.
Here ... | Graphical data overview (summary) function in R | Frank Harrell's Hmisc package has some basic graphics with options for annotation: check out the summary.formula() and related plot wrap functions. I also like the describe() function.
For additional | Graphical data overview (summary) function in R
Frank Harrell's Hmisc package has some basic graphics with options for annotation: check out the summary.formula() and related plot wrap functions. I also like the describe() function.
For additional information, have a look at the The Hmisc Library or An Introduction to ... | Graphical data overview (summary) function in R
Frank Harrell's Hmisc package has some basic graphics with options for annotation: check out the summary.formula() and related plot wrap functions. I also like the describe() function.
For additional |
6,820 | Graphical data overview (summary) function in R | I highly recommend the function chart.Correlations in the package PerformanceAnalytics. It packs an amazing amount of information into a single chart: kernel-density plots and histograms for each variable, and scatterplots, lowess smoothers, and correlations for each variable pair. It's one of my favorite graphical da... | Graphical data overview (summary) function in R | I highly recommend the function chart.Correlations in the package PerformanceAnalytics. It packs an amazing amount of information into a single chart: kernel-density plots and histograms for each var | Graphical data overview (summary) function in R
I highly recommend the function chart.Correlations in the package PerformanceAnalytics. It packs an amazing amount of information into a single chart: kernel-density plots and histograms for each variable, and scatterplots, lowess smoothers, and correlations for each var... | Graphical data overview (summary) function in R
I highly recommend the function chart.Correlations in the package PerformanceAnalytics. It packs an amazing amount of information into a single chart: kernel-density plots and histograms for each var |
6,821 | Graphical data overview (summary) function in R | I have found this function helpful... the original author's handle is respiratoryclub.
f_summary <- function(data_to_plot)
{
## univariate data summary
require(nortest)
#data <- as.numeric(scan ("data.txt")) #commenting out by mike
data <- na.omit(as.numeric(as.character(data_to_plot))) #added by mike
dataFull <- as.n... | Graphical data overview (summary) function in R | I have found this function helpful... the original author's handle is respiratoryclub.
f_summary <- function(data_to_plot)
{
## univariate data summary
require(nortest)
#data <- as.numeric(scan ("dat | Graphical data overview (summary) function in R
I have found this function helpful... the original author's handle is respiratoryclub.
f_summary <- function(data_to_plot)
{
## univariate data summary
require(nortest)
#data <- as.numeric(scan ("data.txt")) #commenting out by mike
data <- na.omit(as.numeric(as.character... | Graphical data overview (summary) function in R
I have found this function helpful... the original author's handle is respiratoryclub.
f_summary <- function(data_to_plot)
{
## univariate data summary
require(nortest)
#data <- as.numeric(scan ("dat |
6,822 | Graphical data overview (summary) function in R | I'm not sure if this is what you were thinking of, but you might want to check out the fitdistrplus package. This has a lot of nice functions that automatically generate useful summary information about your distribution, and make plots of some of that information. Here are some examples from the vignette:
library(... | Graphical data overview (summary) function in R | I'm not sure if this is what you were thinking of, but you might want to check out the fitdistrplus package. This has a lot of nice functions that automatically generate useful summary information ab | Graphical data overview (summary) function in R
I'm not sure if this is what you were thinking of, but you might want to check out the fitdistrplus package. This has a lot of nice functions that automatically generate useful summary information about your distribution, and make plots of some of that information. Here... | Graphical data overview (summary) function in R
I'm not sure if this is what you were thinking of, but you might want to check out the fitdistrplus package. This has a lot of nice functions that automatically generate useful summary information ab |
6,823 | Graphical data overview (summary) function in R | To explore dataset I really like rattle. Install the package and just call rattle(). The interface is quite self explainatory. | Graphical data overview (summary) function in R | To explore dataset I really like rattle. Install the package and just call rattle(). The interface is quite self explainatory. | Graphical data overview (summary) function in R
To explore dataset I really like rattle. Install the package and just call rattle(). The interface is quite self explainatory. | Graphical data overview (summary) function in R
To explore dataset I really like rattle. Install the package and just call rattle(). The interface is quite self explainatory. |
6,824 | Graphical data overview (summary) function in R | Maybe you are looking for the library ggplot2 that lets you plot things in a pretty way.
Or you can check this website that seems to have lots of R graphic utilities
http://addictedtor.free.fr/graphiques/ | Graphical data overview (summary) function in R | Maybe you are looking for the library ggplot2 that lets you plot things in a pretty way.
Or you can check this website that seems to have lots of R graphic utilities
http://addictedtor.free.fr/graphiq | Graphical data overview (summary) function in R
Maybe you are looking for the library ggplot2 that lets you plot things in a pretty way.
Or you can check this website that seems to have lots of R graphic utilities
http://addictedtor.free.fr/graphiques/ | Graphical data overview (summary) function in R
Maybe you are looking for the library ggplot2 that lets you plot things in a pretty way.
Or you can check this website that seems to have lots of R graphic utilities
http://addictedtor.free.fr/graphiq |
6,825 | Graphical data overview (summary) function in R | Its probably not exactly what you are looking for, but the pairs.panels() function in the psych package for R may prove useful. It gives you correlation values in the upper diagonal, loess lines and points in the lower diagonal, and shows a histogram of each variable's scores in the diagonal line of the matrix. I pers... | Graphical data overview (summary) function in R | Its probably not exactly what you are looking for, but the pairs.panels() function in the psych package for R may prove useful. It gives you correlation values in the upper diagonal, loess lines and | Graphical data overview (summary) function in R
Its probably not exactly what you are looking for, but the pairs.panels() function in the psych package for R may prove useful. It gives you correlation values in the upper diagonal, loess lines and points in the lower diagonal, and shows a histogram of each variable's s... | Graphical data overview (summary) function in R
Its probably not exactly what you are looking for, but the pairs.panels() function in the psych package for R may prove useful. It gives you correlation values in the upper diagonal, loess lines and |
6,826 | Graphical data overview (summary) function in R | My favourite is DescTools
library(DescTools)
data("iris")
Desc(iris, plotit = T)
Which produces a series of plots like these:
and displays a series of descriptive values (including mean, meanSE, median, percentiles, range, sd, IQR, values of skewness, and kurtosis):
Alternatively, tabplot is also very good for a gr... | Graphical data overview (summary) function in R | My favourite is DescTools
library(DescTools)
data("iris")
Desc(iris, plotit = T)
Which produces a series of plots like these:
and displays a series of descriptive values (including mean, meanSE, me | Graphical data overview (summary) function in R
My favourite is DescTools
library(DescTools)
data("iris")
Desc(iris, plotit = T)
Which produces a series of plots like these:
and displays a series of descriptive values (including mean, meanSE, median, percentiles, range, sd, IQR, values of skewness, and kurtosis):
A... | Graphical data overview (summary) function in R
My favourite is DescTools
library(DescTools)
data("iris")
Desc(iris, plotit = T)
Which produces a series of plots like these:
and displays a series of descriptive values (including mean, meanSE, me |
6,827 | What is the meaning of the "." (dot) in R? | The dot can be used as in normal name. It has however additional special interpretation. Suppose we have an object with specific class:
a <- list(b=1)
class(a) <- "myclass"
Now declare myfunction as standard generic in the following way:
myfunction <- function(x,...) UseMethod("myfunction")
Now declare the functio... | What is the meaning of the "." (dot) in R? | The dot can be used as in normal name. It has however additional special interpretation. Suppose we have an object with specific class:
a <- list(b=1)
class(a) <- "myclass"
Now declare myfunction a | What is the meaning of the "." (dot) in R?
The dot can be used as in normal name. It has however additional special interpretation. Suppose we have an object with specific class:
a <- list(b=1)
class(a) <- "myclass"
Now declare myfunction as standard generic in the following way:
myfunction <- function(x,...) UseMe... | What is the meaning of the "." (dot) in R?
The dot can be used as in normal name. It has however additional special interpretation. Suppose we have an object with specific class:
a <- list(b=1)
class(a) <- "myclass"
Now declare myfunction a |
6,828 | What is the meaning of the "." (dot) in R? | Look at the help page for ?formula with regard to . Here's the relevant bits:
There are two special interpretations of . in a formula. The usual one
is in the context of a data argument of model fitting functions and
means ‘all columns not otherwise in the formula’: see terms.formula.
In the context of update.fo... | What is the meaning of the "." (dot) in R? | Look at the help page for ?formula with regard to . Here's the relevant bits:
There are two special interpretations of . in a formula. The usual one
is in the context of a data argument of model fi | What is the meaning of the "." (dot) in R?
Look at the help page for ?formula with regard to . Here's the relevant bits:
There are two special interpretations of . in a formula. The usual one
is in the context of a data argument of model fitting functions and
means ‘all columns not otherwise in the formula’: see t... | What is the meaning of the "." (dot) in R?
Look at the help page for ?formula with regard to . Here's the relevant bits:
There are two special interpretations of . in a formula. The usual one
is in the context of a data argument of model fi |
6,829 | What is the meaning of the "." (dot) in R? | There are some exceptions (S3 method dispatch), but generally it is simply used as legibility aid, and as such has no special meaning. | What is the meaning of the "." (dot) in R? | There are some exceptions (S3 method dispatch), but generally it is simply used as legibility aid, and as such has no special meaning. | What is the meaning of the "." (dot) in R?
There are some exceptions (S3 method dispatch), but generally it is simply used as legibility aid, and as such has no special meaning. | What is the meaning of the "." (dot) in R?
There are some exceptions (S3 method dispatch), but generally it is simply used as legibility aid, and as such has no special meaning. |
6,830 | What is the meaning of the "." (dot) in R? | The dot in sample.formula doesn't separate sample from formula, other than visually. It is just a variable name. R variables names can consist of alphanumerics and dot (.) and underscore (_) with one exception. Here is the actual rule:
"A syntactically valid name consists of letters, numbers and the dot or underline c... | What is the meaning of the "." (dot) in R? | The dot in sample.formula doesn't separate sample from formula, other than visually. It is just a variable name. R variables names can consist of alphanumerics and dot (.) and underscore (_) with one | What is the meaning of the "." (dot) in R?
The dot in sample.formula doesn't separate sample from formula, other than visually. It is just a variable name. R variables names can consist of alphanumerics and dot (.) and underscore (_) with one exception. Here is the actual rule:
"A syntactically valid name consists of ... | What is the meaning of the "." (dot) in R?
The dot in sample.formula doesn't separate sample from formula, other than visually. It is just a variable name. R variables names can consist of alphanumerics and dot (.) and underscore (_) with one |
6,831 | Difference between longitudinal design and time series | I will add that in time series context it is usually assumed that data observed is a realisation of stochastic process. Hence in time series a lot of attention is given to properties of stochastic processes, such as stationarity, ergodicity, etc. In longitudinal context in my understanding data comes from usual sample... | Difference between longitudinal design and time series | I will add that in time series context it is usually assumed that data observed is a realisation of stochastic process. Hence in time series a lot of attention is given to properties of stochastic pro | Difference between longitudinal design and time series
I will add that in time series context it is usually assumed that data observed is a realisation of stochastic process. Hence in time series a lot of attention is given to properties of stochastic processes, such as stationarity, ergodicity, etc. In longitudinal co... | Difference between longitudinal design and time series
I will add that in time series context it is usually assumed that data observed is a realisation of stochastic process. Hence in time series a lot of attention is given to properties of stochastic pro |
6,832 | Difference between longitudinal design and time series | If we think of designs made up of $n$ cases measured on $k$ occasions, then the following loose definition seems to me to be descriptive of the distinction:
longitudinal designs: high $n$, low $k$
time series: low $n$, high $k$
Of course, this raises the question of what is high and what is low.
Summarising my own ro... | Difference between longitudinal design and time series | If we think of designs made up of $n$ cases measured on $k$ occasions, then the following loose definition seems to me to be descriptive of the distinction:
longitudinal designs: high $n$, low $k$
ti | Difference between longitudinal design and time series
If we think of designs made up of $n$ cases measured on $k$ occasions, then the following loose definition seems to me to be descriptive of the distinction:
longitudinal designs: high $n$, low $k$
time series: low $n$, high $k$
Of course, this raises the question... | Difference between longitudinal design and time series
If we think of designs made up of $n$ cases measured on $k$ occasions, then the following loose definition seems to me to be descriptive of the distinction:
longitudinal designs: high $n$, low $k$
ti |
6,833 | Difference between longitudinal design and time series | A time series is simple a sequence of data points spaced out over time, usually with regular time intervals. A longitudinal design is rather more specific, keeping the same sample for each observation over time.
An example of a time series might be unemployment measured every month using a labour force survey with a n... | Difference between longitudinal design and time series | A time series is simple a sequence of data points spaced out over time, usually with regular time intervals. A longitudinal design is rather more specific, keeping the same sample for each observatio | Difference between longitudinal design and time series
A time series is simple a sequence of data points spaced out over time, usually with regular time intervals. A longitudinal design is rather more specific, keeping the same sample for each observation over time.
An example of a time series might be unemployment me... | Difference between longitudinal design and time series
A time series is simple a sequence of data points spaced out over time, usually with regular time intervals. A longitudinal design is rather more specific, keeping the same sample for each observatio |
6,834 | Difference between longitudinal design and time series | Time-series data are assessed at regular intervals for a long period of time. Whereas longitudinal data are not: the repeated measures are for a short period of time. That is data collection can stop / be terminated at a certain point in time to do the analysis or when the measures satisfies the researcher in terms of ... | Difference between longitudinal design and time series | Time-series data are assessed at regular intervals for a long period of time. Whereas longitudinal data are not: the repeated measures are for a short period of time. That is data collection can stop | Difference between longitudinal design and time series
Time-series data are assessed at regular intervals for a long period of time. Whereas longitudinal data are not: the repeated measures are for a short period of time. That is data collection can stop / be terminated at a certain point in time to do the analysis or ... | Difference between longitudinal design and time series
Time-series data are assessed at regular intervals for a long period of time. Whereas longitudinal data are not: the repeated measures are for a short period of time. That is data collection can stop |
6,835 | How to interpret variance and correlation of random effects in a mixed-effects model? | Your fitted model with lme() can be expressed as
$y_{ij} = \alpha_0 + \alpha_1 x_j + \delta_{0i} + \delta_{1i} x_j + \epsilon_{ij}$
where $y_{ij}$ is the score of $i$th employee at $x_j$ weeks, $\alpha_0$ and $\alpha_1$ are the fixed intercept and slope respectively, $\delta_{0i}$ and $\delta_{1i}$ are the random inter... | How to interpret variance and correlation of random effects in a mixed-effects model? | Your fitted model with lme() can be expressed as
$y_{ij} = \alpha_0 + \alpha_1 x_j + \delta_{0i} + \delta_{1i} x_j + \epsilon_{ij}$
where $y_{ij}$ is the score of $i$th employee at $x_j$ weeks, $\alph | How to interpret variance and correlation of random effects in a mixed-effects model?
Your fitted model with lme() can be expressed as
$y_{ij} = \alpha_0 + \alpha_1 x_j + \delta_{0i} + \delta_{1i} x_j + \epsilon_{ij}$
where $y_{ij}$ is the score of $i$th employee at $x_j$ weeks, $\alpha_0$ and $\alpha_1$ are the fixed ... | How to interpret variance and correlation of random effects in a mixed-effects model?
Your fitted model with lme() can be expressed as
$y_{ij} = \alpha_0 + \alpha_1 x_j + \delta_{0i} + \delta_{1i} x_j + \epsilon_{ij}$
where $y_{ij}$ is the score of $i$th employee at $x_j$ weeks, $\alph |
6,836 | Derivation of change of variables of a probability density function? | Suppose $X$ is a continuous random variable with pdf $f$.
Let $Y=g(X)$, where $g$ is a monotonic function.
The function $g$ could be either monotonically increasing or monotonically decreasing. If $g$ were monotonically increasing, then the pdf of $Y$ is obtained as follows:
\begin{eqnarray*}
P(Y\le y) &=& P(g(X)\le y... | Derivation of change of variables of a probability density function? | Suppose $X$ is a continuous random variable with pdf $f$.
Let $Y=g(X)$, where $g$ is a monotonic function.
The function $g$ could be either monotonically increasing or monotonically decreasing. If $g$ | Derivation of change of variables of a probability density function?
Suppose $X$ is a continuous random variable with pdf $f$.
Let $Y=g(X)$, where $g$ is a monotonic function.
The function $g$ could be either monotonically increasing or monotonically decreasing. If $g$ were monotonically increasing, then the pdf of $Y$... | Derivation of change of variables of a probability density function?
Suppose $X$ is a continuous random variable with pdf $f$.
Let $Y=g(X)$, where $g$ is a monotonic function.
The function $g$ could be either monotonically increasing or monotonically decreasing. If $g$ |
6,837 | What does entropy tell us? | The entropy tells you how much uncertainty is in the system. Let's say you're looking for a cat, and you know that it's somewhere between your house and the neighbors, which is 1 mile away. Your kids tell you that the probability of a cat being on the distance $x$ from your house is described best by beta distribution ... | What does entropy tell us? | The entropy tells you how much uncertainty is in the system. Let's say you're looking for a cat, and you know that it's somewhere between your house and the neighbors, which is 1 mile away. Your kids | What does entropy tell us?
The entropy tells you how much uncertainty is in the system. Let's say you're looking for a cat, and you know that it's somewhere between your house and the neighbors, which is 1 mile away. Your kids tell you that the probability of a cat being on the distance $x$ from your house is described... | What does entropy tell us?
The entropy tells you how much uncertainty is in the system. Let's say you're looking for a cat, and you know that it's somewhere between your house and the neighbors, which is 1 mile away. Your kids |
6,838 | What does entropy tell us? | what does that quantity actually tell me?
I'd like to plug in a straightforward answer as follows:
It's intuitive to illustrate that in a discrete scenario. Suppose that you toss a heavily biased coin, saying the probability of seeing head on each flip is 0.99. Every actual flip tells you very little information beca... | What does entropy tell us? | what does that quantity actually tell me?
I'd like to plug in a straightforward answer as follows:
It's intuitive to illustrate that in a discrete scenario. Suppose that you toss a heavily biased co | What does entropy tell us?
what does that quantity actually tell me?
I'd like to plug in a straightforward answer as follows:
It's intuitive to illustrate that in a discrete scenario. Suppose that you toss a heavily biased coin, saying the probability of seeing head on each flip is 0.99. Every actual flip tells you v... | What does entropy tell us?
what does that quantity actually tell me?
I'd like to plug in a straightforward answer as follows:
It's intuitive to illustrate that in a discrete scenario. Suppose that you toss a heavily biased co |
6,839 | What does entropy tell us? | I don't feel that the most of the answers provided above are answering the question posed, except the comment by whuber. If I understand correctly, the original question pertains to DISCRETE cases as opposed the continuous cases. My impression is that RustyStatistician knows well what entropy means in discrete cases b... | What does entropy tell us? | I don't feel that the most of the answers provided above are answering the question posed, except the comment by whuber. If I understand correctly, the original question pertains to DISCRETE cases as | What does entropy tell us?
I don't feel that the most of the answers provided above are answering the question posed, except the comment by whuber. If I understand correctly, the original question pertains to DISCRETE cases as opposed the continuous cases. My impression is that RustyStatistician knows well what entrop... | What does entropy tell us?
I don't feel that the most of the answers provided above are answering the question posed, except the comment by whuber. If I understand correctly, the original question pertains to DISCRETE cases as |
6,840 | Is there any algorithm combining classification and regression? | The problem that you are describing can be solved by latent class regression, or cluster-wise regression, or it's extension mixture of generalized linear models that are all members of a wider family of finite mixture models, or latent class models.
It's not a combination of classification (supervised learning) and reg... | Is there any algorithm combining classification and regression? | The problem that you are describing can be solved by latent class regression, or cluster-wise regression, or it's extension mixture of generalized linear models that are all members of a wider family | Is there any algorithm combining classification and regression?
The problem that you are describing can be solved by latent class regression, or cluster-wise regression, or it's extension mixture of generalized linear models that are all members of a wider family of finite mixture models, or latent class models.
It's n... | Is there any algorithm combining classification and regression?
The problem that you are describing can be solved by latent class regression, or cluster-wise regression, or it's extension mixture of generalized linear models that are all members of a wider family |
6,841 | Is there any algorithm combining classification and regression? | Multi-Task Learning MLT allows different types of loss-functions ( for example, least-square for regression and logistic or Hinge loss for classification) to be optimized simultaneously.
the components of this heterogeneous loss function can be weighted to control/ distinguish the main task from the secondary one. if t... | Is there any algorithm combining classification and regression? | Multi-Task Learning MLT allows different types of loss-functions ( for example, least-square for regression and logistic or Hinge loss for classification) to be optimized simultaneously.
the component | Is there any algorithm combining classification and regression?
Multi-Task Learning MLT allows different types of loss-functions ( for example, least-square for regression and logistic or Hinge loss for classification) to be optimized simultaneously.
the components of this heterogeneous loss function can be weighted to... | Is there any algorithm combining classification and regression?
Multi-Task Learning MLT allows different types of loss-functions ( for example, least-square for regression and logistic or Hinge loss for classification) to be optimized simultaneously.
the component |
6,842 | How do I know which method of cross validation is best? | Since the OP has placed a bounty on this question, it should attract some attention, and thus it is the right place to discuss some general ideas, even if it does not answer the OP directly.
First, names:
a) cross-validation is the general name for all estimation/measure techniques that use a test set different than t... | How do I know which method of cross validation is best? | Since the OP has placed a bounty on this question, it should attract some attention, and thus it is the right place to discuss some general ideas, even if it does not answer the OP directly.
First, na | How do I know which method of cross validation is best?
Since the OP has placed a bounty on this question, it should attract some attention, and thus it is the right place to discuss some general ideas, even if it does not answer the OP directly.
First, names:
a) cross-validation is the general name for all estimation/... | How do I know which method of cross validation is best?
Since the OP has placed a bounty on this question, it should attract some attention, and thus it is the right place to discuss some general ideas, even if it does not answer the OP directly.
First, na |
6,843 | How do I know which method of cross validation is best? | Please refer to the wikipedia page for the method definitions (they do a far better job than I could do here).
After you have had a look at that page, the following may be of help to you. Let me focus on the part of the question where one wants to pick one of these methods for their modeling process. Since this is pre... | How do I know which method of cross validation is best? | Please refer to the wikipedia page for the method definitions (they do a far better job than I could do here).
After you have had a look at that page, the following may be of help to you. Let me focu | How do I know which method of cross validation is best?
Please refer to the wikipedia page for the method definitions (they do a far better job than I could do here).
After you have had a look at that page, the following may be of help to you. Let me focus on the part of the question where one wants to pick one of the... | How do I know which method of cross validation is best?
Please refer to the wikipedia page for the method definitions (they do a far better job than I could do here).
After you have had a look at that page, the following may be of help to you. Let me focu |
6,844 | Which statistical model is being used in the Pfizer study design for vaccine efficacy? | The relation between efficiency and illness risk ratio
I want to know why vaccine efficacy is defined as illustrated at the bottom of this page:
$$ \text{VE} = 1 - \text{IRR}$$
where
$$ \text{IRR} = \frac{\text{illness rate in vaccine group}}{\text{illness rate in placebo group}}$$
This is just a definition. Possibly... | Which statistical model is being used in the Pfizer study design for vaccine efficacy? | The relation between efficiency and illness risk ratio
I want to know why vaccine efficacy is defined as illustrated at the bottom of this page:
$$ \text{VE} = 1 - \text{IRR}$$
where
$$ \text{IRR} = | Which statistical model is being used in the Pfizer study design for vaccine efficacy?
The relation between efficiency and illness risk ratio
I want to know why vaccine efficacy is defined as illustrated at the bottom of this page:
$$ \text{VE} = 1 - \text{IRR}$$
where
$$ \text{IRR} = \frac{\text{illness rate in vacci... | Which statistical model is being used in the Pfizer study design for vaccine efficacy?
The relation between efficiency and illness risk ratio
I want to know why vaccine efficacy is defined as illustrated at the bottom of this page:
$$ \text{VE} = 1 - \text{IRR}$$
where
$$ \text{IRR} = |
6,845 | Which statistical model is being used in the Pfizer study design for vaccine efficacy? | All these results are consistent with using the conditional Maximum Likelihood Estimate as implemented in the base R implementation of the fisher's exact test:
splits <- matrix(c(6,26,15,47,25,67,35,85,53,111), ncol = 2, byrow = T)
total <- 43000
for(interim in 1:nrow(splits)) {
positive_vax <- splits[interim, 1]
... | Which statistical model is being used in the Pfizer study design for vaccine efficacy? | All these results are consistent with using the conditional Maximum Likelihood Estimate as implemented in the base R implementation of the fisher's exact test:
splits <- matrix(c(6,26,15,47,25,67,35,8 | Which statistical model is being used in the Pfizer study design for vaccine efficacy?
All these results are consistent with using the conditional Maximum Likelihood Estimate as implemented in the base R implementation of the fisher's exact test:
splits <- matrix(c(6,26,15,47,25,67,35,85,53,111), ncol = 2, byrow = T)
t... | Which statistical model is being used in the Pfizer study design for vaccine efficacy?
All these results are consistent with using the conditional Maximum Likelihood Estimate as implemented in the base R implementation of the fisher's exact test:
splits <- matrix(c(6,26,15,47,25,67,35,8 |
6,846 | Why use stratified cross validation? Why does this not damage variance related benefit? | Bootstrapping seeks to simulate the effect of drawing a new sample from the population, and doesn't seek to ensure distinct test sets (residues after N from N sampling with replacement).
RxK-fold Cross-validation ensures K distinct test folds but is then repeated R times for different random partitionings to allow inde... | Why use stratified cross validation? Why does this not damage variance related benefit? | Bootstrapping seeks to simulate the effect of drawing a new sample from the population, and doesn't seek to ensure distinct test sets (residues after N from N sampling with replacement).
RxK-fold Cros | Why use stratified cross validation? Why does this not damage variance related benefit?
Bootstrapping seeks to simulate the effect of drawing a new sample from the population, and doesn't seek to ensure distinct test sets (residues after N from N sampling with replacement).
RxK-fold Cross-validation ensures K distinct ... | Why use stratified cross validation? Why does this not damage variance related benefit?
Bootstrapping seeks to simulate the effect of drawing a new sample from the population, and doesn't seek to ensure distinct test sets (residues after N from N sampling with replacement).
RxK-fold Cros |
6,847 | Why use stratified cross validation? Why does this not damage variance related benefit? | Perhaps you can think of it this way. Let's say you have a dataset where there are 100 samples, 90 in class 'A' and 10 in class 'B'. In this very unbalanced design if you do normal randomized groups, you could end up building models on exceedingly few (or EVEN NONE!) from the 'B' class. If you are building a model t... | Why use stratified cross validation? Why does this not damage variance related benefit? | Perhaps you can think of it this way. Let's say you have a dataset where there are 100 samples, 90 in class 'A' and 10 in class 'B'. In this very unbalanced design if you do normal randomized groups | Why use stratified cross validation? Why does this not damage variance related benefit?
Perhaps you can think of it this way. Let's say you have a dataset where there are 100 samples, 90 in class 'A' and 10 in class 'B'. In this very unbalanced design if you do normal randomized groups, you could end up building mode... | Why use stratified cross validation? Why does this not damage variance related benefit?
Perhaps you can think of it this way. Let's say you have a dataset where there are 100 samples, 90 in class 'A' and 10 in class 'B'. In this very unbalanced design if you do normal randomized groups |
6,848 | How do I interpret the 'correlations of fixed effects' in my glmer output? | The "correlation of fixed effects" output doesn't have the intuitive meaning that most would ascribe to it. Specifically, is not about the correlation of the variables (as OP notes). It is in fact about the expected correlation of the regression coefficients. Although this may speak to multicollinearity it does not n... | How do I interpret the 'correlations of fixed effects' in my glmer output? | The "correlation of fixed effects" output doesn't have the intuitive meaning that most would ascribe to it. Specifically, is not about the correlation of the variables (as OP notes). It is in fact ab | How do I interpret the 'correlations of fixed effects' in my glmer output?
The "correlation of fixed effects" output doesn't have the intuitive meaning that most would ascribe to it. Specifically, is not about the correlation of the variables (as OP notes). It is in fact about the expected correlation of the regressio... | How do I interpret the 'correlations of fixed effects' in my glmer output?
The "correlation of fixed effects" output doesn't have the intuitive meaning that most would ascribe to it. Specifically, is not about the correlation of the variables (as OP notes). It is in fact ab |
6,849 | How do I interpret the 'correlations of fixed effects' in my glmer output? | It can be helpful to show that those correlations between fixed effects are obtained by converting the model's "vcov" to a correlation matrix. If fit is your fitted lme4 model, then
vc <- vcov(fit)
# diagonal matrix of standard deviations associated with vcov
S <- sqrt(diag(diag(vc), nrow(vc), nrow(vc)))
# convert vc... | How do I interpret the 'correlations of fixed effects' in my glmer output? | It can be helpful to show that those correlations between fixed effects are obtained by converting the model's "vcov" to a correlation matrix. If fit is your fitted lme4 model, then
vc <- vcov(fit)
# | How do I interpret the 'correlations of fixed effects' in my glmer output?
It can be helpful to show that those correlations between fixed effects are obtained by converting the model's "vcov" to a correlation matrix. If fit is your fitted lme4 model, then
vc <- vcov(fit)
# diagonal matrix of standard deviations assoc... | How do I interpret the 'correlations of fixed effects' in my glmer output?
It can be helpful to show that those correlations between fixed effects are obtained by converting the model's "vcov" to a correlation matrix. If fit is your fitted lme4 model, then
vc <- vcov(fit)
# |
6,850 | How do I interpret the 'correlations of fixed effects' in my glmer output? | If your negative and positive correlations are the same in their value and only their sign differ, you are entering the variable mistakenly. But I don't think this is the case for you as you already seem quite advanced in stats.
The inconsistency you are experiencing can be and is likely caused by multicollinearity. It... | How do I interpret the 'correlations of fixed effects' in my glmer output? | If your negative and positive correlations are the same in their value and only their sign differ, you are entering the variable mistakenly. But I don't think this is the case for you as you already s | How do I interpret the 'correlations of fixed effects' in my glmer output?
If your negative and positive correlations are the same in their value and only their sign differ, you are entering the variable mistakenly. But I don't think this is the case for you as you already seem quite advanced in stats.
The inconsistenc... | How do I interpret the 'correlations of fixed effects' in my glmer output?
If your negative and positive correlations are the same in their value and only their sign differ, you are entering the variable mistakenly. But I don't think this is the case for you as you already s |
6,851 | Application of machine learning methods in StackExchange websites | Yes, I think tag prediction is an interesting one and one for which you have a good shot at "success".
Below are some thoughts intended to potentially aid in brainstorming and further exploration of this topic. I think there are many potentially interesting directions that such a project could take. I would guess that... | Application of machine learning methods in StackExchange websites | Yes, I think tag prediction is an interesting one and one for which you have a good shot at "success".
Below are some thoughts intended to potentially aid in brainstorming and further exploration of | Application of machine learning methods in StackExchange websites
Yes, I think tag prediction is an interesting one and one for which you have a good shot at "success".
Below are some thoughts intended to potentially aid in brainstorming and further exploration of this topic. I think there are many potentially interes... | Application of machine learning methods in StackExchange websites
Yes, I think tag prediction is an interesting one and one for which you have a good shot at "success".
Below are some thoughts intended to potentially aid in brainstorming and further exploration of |
6,852 | Application of machine learning methods in StackExchange websites | I was thinking about tag prediction, too, I like the idea. I have the feeling that it is possible, but you may need to overcome many issues before you arrive to your final dataset. So I speculate the tag prediction may need a lot of time. In addition to incorrect tags the limit of max 5 tags may play a role. Also that ... | Application of machine learning methods in StackExchange websites | I was thinking about tag prediction, too, I like the idea. I have the feeling that it is possible, but you may need to overcome many issues before you arrive to your final dataset. So I speculate the | Application of machine learning methods in StackExchange websites
I was thinking about tag prediction, too, I like the idea. I have the feeling that it is possible, but you may need to overcome many issues before you arrive to your final dataset. So I speculate the tag prediction may need a lot of time. In addition to ... | Application of machine learning methods in StackExchange websites
I was thinking about tag prediction, too, I like the idea. I have the feeling that it is possible, but you may need to overcome many issues before you arrive to your final dataset. So I speculate the |
6,853 | Application of machine learning methods in StackExchange websites | This is a good question. I too have thought that the publicly available StackExchange datasets would make good subjects for analysis. These are sufficiently unusual that they might also be good testbeds for new statistical methods. Having such a large amount of well structured data is unusual, at any rate.
cardinal sug... | Application of machine learning methods in StackExchange websites | This is a good question. I too have thought that the publicly available StackExchange datasets would make good subjects for analysis. These are sufficiently unusual that they might also be good testbe | Application of machine learning methods in StackExchange websites
This is a good question. I too have thought that the publicly available StackExchange datasets would make good subjects for analysis. These are sufficiently unusual that they might also be good testbeds for new statistical methods. Having such a large am... | Application of machine learning methods in StackExchange websites
This is a good question. I too have thought that the publicly available StackExchange datasets would make good subjects for analysis. These are sufficiently unusual that they might also be good testbe |
6,854 | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy? | It's a good question. Generally, I would argue that you should try to optimise a loss function which corresponds to the evaluation metric you care most about.
You might however want to know about other evaluation metrics.
For example, when doing classification, I'm of the opinion that you would need to give me a pretty... | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy? | It's a good question. Generally, I would argue that you should try to optimise a loss function which corresponds to the evaluation metric you care most about.
You might however want to know about othe | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy?
It's a good question. Generally, I would argue that you should try to optimise a loss function which corresponds to the evaluation metric you care most about.
You might however want to know about other evaluation metrics.
For ... | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy?
It's a good question. Generally, I would argue that you should try to optimise a loss function which corresponds to the evaluation metric you care most about.
You might however want to know about othe |
6,855 | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy? | Often, MSE/cross-entropy are easier to optimize than for accuracy, because they are differentiable wrt to the model parameters, and in some cases, even convex, which makes it a lot easier.
Even in cases where the metric is differentiable, you might want a loss which has "better behaved" numerical properties -- see thi... | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy? | Often, MSE/cross-entropy are easier to optimize than for accuracy, because they are differentiable wrt to the model parameters, and in some cases, even convex, which makes it a lot easier.
Even in ca | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy?
Often, MSE/cross-entropy are easier to optimize than for accuracy, because they are differentiable wrt to the model parameters, and in some cases, even convex, which makes it a lot easier.
Even in cases where the metric is di... | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy?
Often, MSE/cross-entropy are easier to optimize than for accuracy, because they are differentiable wrt to the model parameters, and in some cases, even convex, which makes it a lot easier.
Even in ca |
6,856 | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy? | It is a good question. I would like to answer it from a different perspective. Assume your model's intent is to find out the price of a house given a host of features (location, num of bedrooms, area etc). So you train a model. Your peer and competitor in same company also develops another model. Now both of you go to ... | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy? | It is a good question. I would like to answer it from a different perspective. Assume your model's intent is to find out the price of a house given a host of features (location, num of bedrooms, area | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy?
It is a good question. I would like to answer it from a different perspective. Assume your model's intent is to find out the price of a house given a host of features (location, num of bedrooms, area etc). So you train a model... | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy?
It is a good question. I would like to answer it from a different perspective. Assume your model's intent is to find out the price of a house given a host of features (location, num of bedrooms, area |
6,857 | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy? | The differences are:
A loss function is used to train your model. A metric is used to evaluate your model.
A loss function is used during the learning process. A metric is used after the learning process
Example: Assuming you train three different models each using different algorithms and loss function to solve the ... | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy? | The differences are:
A loss function is used to train your model. A metric is used to evaluate your model.
A loss function is used during the learning process. A metric is used after the learning pro | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy?
The differences are:
A loss function is used to train your model. A metric is used to evaluate your model.
A loss function is used during the learning process. A metric is used after the learning process
Example: Assuming yo... | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy?
The differences are:
A loss function is used to train your model. A metric is used to evaluate your model.
A loss function is used during the learning process. A metric is used after the learning pro |
6,858 | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy? | As explained here in relation to the no free lunch theorem (https://peekaboo-vision.blogspot.com/2019/07/dont-cite-no-free-lunch-theorem.html, see the final part), “learning is impossible without proper assumptions”. If you have a model which simply minimizes the evaluation metric, you are not making any assumption abo... | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy? | As explained here in relation to the no free lunch theorem (https://peekaboo-vision.blogspot.com/2019/07/dont-cite-no-free-lunch-theorem.html, see the final part), “learning is impossible without prop | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy?
As explained here in relation to the no free lunch theorem (https://peekaboo-vision.blogspot.com/2019/07/dont-cite-no-free-lunch-theorem.html, see the final part), “learning is impossible without proper assumptions”. If you ha... | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy?
As explained here in relation to the no free lunch theorem (https://peekaboo-vision.blogspot.com/2019/07/dont-cite-no-free-lunch-theorem.html, see the final part), “learning is impossible without prop |
6,859 | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy? | There is no learning theoretical requirement for loss function and test-evaluation metric mismatch. An argument for such a practice however :
The learned function is so well generalised, even there is a mismatch between loss function and the testing evaluation metric, one could still get a good performance.
Hyper-para... | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy? | There is no learning theoretical requirement for loss function and test-evaluation metric mismatch. An argument for such a practice however :
The learned function is so well generalised, even there i | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy?
There is no learning theoretical requirement for loss function and test-evaluation metric mismatch. An argument for such a practice however :
The learned function is so well generalised, even there is a mismatch between loss ... | Why do we use loss functions to estimate a model instead of evaluation metrics like accuracy?
There is no learning theoretical requirement for loss function and test-evaluation metric mismatch. An argument for such a practice however :
The learned function is so well generalised, even there i |
6,860 | Information gain, mutual information and related measures | I think that calling the Kullback-Leibler divergence "information gain" is non-standard.
The first definition is standard.
EDIT: However, $H(Y)−H(Y|X)$ can also be called mutual information.
Note that I don't think you will find any scientific discipline that really has a standardized, precise, and consistent naming s... | Information gain, mutual information and related measures | I think that calling the Kullback-Leibler divergence "information gain" is non-standard.
The first definition is standard.
EDIT: However, $H(Y)−H(Y|X)$ can also be called mutual information.
Note tha | Information gain, mutual information and related measures
I think that calling the Kullback-Leibler divergence "information gain" is non-standard.
The first definition is standard.
EDIT: However, $H(Y)−H(Y|X)$ can also be called mutual information.
Note that I don't think you will find any scientific discipline that r... | Information gain, mutual information and related measures
I think that calling the Kullback-Leibler divergence "information gain" is non-standard.
The first definition is standard.
EDIT: However, $H(Y)−H(Y|X)$ can also be called mutual information.
Note tha |
6,861 | Information gain, mutual information and related measures | The Kullback-Leiber Divergence between $p(X,Y)$ and $P(X)P(Y)$ is the same as mutual information, which can be easily derived:
$$
\begin{align}
I(X; Y) &= H(Y) - H(Y \mid X)\\
&= - \sum_y p(y) \log p(y) + \sum_{x,y} p(x) p(y\mid x) \log p(y\mid x)\\
&= \sum_{x,y} p(x, y) \log{p(y\mid x)} - \sum_{y} \left(\sum_{x}p(x,y... | Information gain, mutual information and related measures | The Kullback-Leiber Divergence between $p(X,Y)$ and $P(X)P(Y)$ is the same as mutual information, which can be easily derived:
$$
\begin{align}
I(X; Y) &= H(Y) - H(Y \mid X)\\
&= - \sum_y p(y) \log p | Information gain, mutual information and related measures
The Kullback-Leiber Divergence between $p(X,Y)$ and $P(X)P(Y)$ is the same as mutual information, which can be easily derived:
$$
\begin{align}
I(X; Y) &= H(Y) - H(Y \mid X)\\
&= - \sum_y p(y) \log p(y) + \sum_{x,y} p(x) p(y\mid x) \log p(y\mid x)\\
&= \sum_{x,... | Information gain, mutual information and related measures
The Kullback-Leiber Divergence between $p(X,Y)$ and $P(X)P(Y)$ is the same as mutual information, which can be easily derived:
$$
\begin{align}
I(X; Y) &= H(Y) - H(Y \mid X)\\
&= - \sum_y p(y) \log p |
6,862 | Information gain, mutual information and related measures | Both definitions are correct, and consistent. I'm not sure what you find unclear as you point out multiple points that might need clarification.
Firstly: $MI_{Mutual Information}\equiv$ $IG_{InformationGain}\equiv I_{Information}$ are all different names for the same thing. In different contexts one of these names may... | Information gain, mutual information and related measures | Both definitions are correct, and consistent. I'm not sure what you find unclear as you point out multiple points that might need clarification.
Firstly: $MI_{Mutual Information}\equiv$ $IG_{Informati | Information gain, mutual information and related measures
Both definitions are correct, and consistent. I'm not sure what you find unclear as you point out multiple points that might need clarification.
Firstly: $MI_{Mutual Information}\equiv$ $IG_{InformationGain}\equiv I_{Information}$ are all different names for the... | Information gain, mutual information and related measures
Both definitions are correct, and consistent. I'm not sure what you find unclear as you point out multiple points that might need clarification.
Firstly: $MI_{Mutual Information}\equiv$ $IG_{Informati |
6,863 | Information gain, mutual information and related measures | Mutual information can be defined using Kullback-Liebler as
\begin{align*}
I(X;Y) = D_{KL}(p(x,y)||p(x)p(y)).
\end{align*} | Information gain, mutual information and related measures | Mutual information can be defined using Kullback-Liebler as
\begin{align*}
I(X;Y) = D_{KL}(p(x,y)||p(x)p(y)).
\end{align*} | Information gain, mutual information and related measures
Mutual information can be defined using Kullback-Liebler as
\begin{align*}
I(X;Y) = D_{KL}(p(x,y)||p(x)p(y)).
\end{align*} | Information gain, mutual information and related measures
Mutual information can be defined using Kullback-Liebler as
\begin{align*}
I(X;Y) = D_{KL}(p(x,y)||p(x)p(y)).
\end{align*} |
6,864 | Understanding p-value | First answer
You have to think at the concept of extreme in terms of probability of the test statistics, not in terms of its value or the value of the random variable being tested. I report the following example from
Christensen, R. (2005). Testing Fisher, Neyman, Pearson, and Bayes. The American Statistician, 59(2), 1... | Understanding p-value | First answer
You have to think at the concept of extreme in terms of probability of the test statistics, not in terms of its value or the value of the random variable being tested. I report the follow | Understanding p-value
First answer
You have to think at the concept of extreme in terms of probability of the test statistics, not in terms of its value or the value of the random variable being tested. I report the following example from
Christensen, R. (2005). Testing Fisher, Neyman, Pearson, and Bayes. The American ... | Understanding p-value
First answer
You have to think at the concept of extreme in terms of probability of the test statistics, not in terms of its value or the value of the random variable being tested. I report the follow |
6,865 | Understanding p-value | (1) A statistic is a number you can calculate from a sample. It's used to put into order all the samples you might have got (under an assumed model, where coins don't land on their edges & what have you). If $t$ is what you calculate from the sample you actually got, & $T$ is the corresponding random variable, then t... | Understanding p-value | (1) A statistic is a number you can calculate from a sample. It's used to put into order all the samples you might have got (under an assumed model, where coins don't land on their edges & what have | Understanding p-value
(1) A statistic is a number you can calculate from a sample. It's used to put into order all the samples you might have got (under an assumed model, where coins don't land on their edges & what have you). If $t$ is what you calculate from the sample you actually got, & $T$ is the corresponding r... | Understanding p-value
(1) A statistic is a number you can calculate from a sample. It's used to put into order all the samples you might have got (under an assumed model, where coins don't land on their edges & what have |
6,866 | What does the Akaike Information Criterion (AIC) score of a model mean? | This question by caveman is popular, but there were no attempted answers for months until my controversial one. It may be that the actual answer below is not, in itself, controversial, merely that the questions are "loaded" questions, because the field seems (to me, at least) to be populated by acolytes of AIC and BIC... | What does the Akaike Information Criterion (AIC) score of a model mean? | This question by caveman is popular, but there were no attempted answers for months until my controversial one. It may be that the actual answer below is not, in itself, controversial, merely that th | What does the Akaike Information Criterion (AIC) score of a model mean?
This question by caveman is popular, but there were no attempted answers for months until my controversial one. It may be that the actual answer below is not, in itself, controversial, merely that the questions are "loaded" questions, because the ... | What does the Akaike Information Criterion (AIC) score of a model mean?
This question by caveman is popular, but there were no attempted answers for months until my controversial one. It may be that the actual answer below is not, in itself, controversial, merely that th |
6,867 | What does the Akaike Information Criterion (AIC) score of a model mean? | AIC is an estimate of twice the model-driven additive term to the expected Kullback-Leibler divergence between the true distribution $f$ and the approximating parametric model $g$.
K-L divergence is a topic in information theory and works intuitively (though not rigorously) as a measure of distance between two probabil... | What does the Akaike Information Criterion (AIC) score of a model mean? | AIC is an estimate of twice the model-driven additive term to the expected Kullback-Leibler divergence between the true distribution $f$ and the approximating parametric model $g$.
K-L divergence is a | What does the Akaike Information Criterion (AIC) score of a model mean?
AIC is an estimate of twice the model-driven additive term to the expected Kullback-Leibler divergence between the true distribution $f$ and the approximating parametric model $g$.
K-L divergence is a topic in information theory and works intuitive... | What does the Akaike Information Criterion (AIC) score of a model mean?
AIC is an estimate of twice the model-driven additive term to the expected Kullback-Leibler divergence between the true distribution $f$ and the approximating parametric model $g$.
K-L divergence is a |
6,868 | What does the Akaike Information Criterion (AIC) score of a model mean? | A simple point of view for your first two questions is that the AIC is related to the expected out-of-sample error rate of the maximum likelihood model. The AIC criterion is based on the relationship (Elements of Statistical Learning equation 7.27)
$$
-2 \, \mathrm{E}[\ln \mathrm{Pr}(D|\theta)] \approx -\frac{2}{N} \, ... | What does the Akaike Information Criterion (AIC) score of a model mean? | A simple point of view for your first two questions is that the AIC is related to the expected out-of-sample error rate of the maximum likelihood model. The AIC criterion is based on the relationship | What does the Akaike Information Criterion (AIC) score of a model mean?
A simple point of view for your first two questions is that the AIC is related to the expected out-of-sample error rate of the maximum likelihood model. The AIC criterion is based on the relationship (Elements of Statistical Learning equation 7.27)... | What does the Akaike Information Criterion (AIC) score of a model mean?
A simple point of view for your first two questions is that the AIC is related to the expected out-of-sample error rate of the maximum likelihood model. The AIC criterion is based on the relationship |
6,869 | How to derive the standard error of linear regression coefficient | 3rd comment above: I've already understand how it comes. But still a question: in my post, the standard error has (n−2), where according to your answer, it doesn't, why?
In my post, it is found that
$$
\widehat{\text{se}}(\hat{b}) = \sqrt{\frac{n \hat{\sigma}^2}{n\sum x_i^2 - (\sum x_i)^2}}.
$$
The denominator can be... | How to derive the standard error of linear regression coefficient | 3rd comment above: I've already understand how it comes. But still a question: in my post, the standard error has (n−2), where according to your answer, it doesn't, why?
In my post, it is found that | How to derive the standard error of linear regression coefficient
3rd comment above: I've already understand how it comes. But still a question: in my post, the standard error has (n−2), where according to your answer, it doesn't, why?
In my post, it is found that
$$
\widehat{\text{se}}(\hat{b}) = \sqrt{\frac{n \hat{... | How to derive the standard error of linear regression coefficient
3rd comment above: I've already understand how it comes. But still a question: in my post, the standard error has (n−2), where according to your answer, it doesn't, why?
In my post, it is found that |
6,870 | How to derive the standard error of linear regression coefficient | another way of thinking about the n-2 df is that it's because we use 2 means to estimate the slope coefficient (the mean of Y and X)
df from Wikipedia: "...In general, the degrees of freedom of an estimate of a parameter are equal to the number of independent scores that go into the estimate minus the number of paramet... | How to derive the standard error of linear regression coefficient | another way of thinking about the n-2 df is that it's because we use 2 means to estimate the slope coefficient (the mean of Y and X)
df from Wikipedia: "...In general, the degrees of freedom of an est | How to derive the standard error of linear regression coefficient
another way of thinking about the n-2 df is that it's because we use 2 means to estimate the slope coefficient (the mean of Y and X)
df from Wikipedia: "...In general, the degrees of freedom of an estimate of a parameter are equal to the number of indepe... | How to derive the standard error of linear regression coefficient
another way of thinking about the n-2 df is that it's because we use 2 means to estimate the slope coefficient (the mean of Y and X)
df from Wikipedia: "...In general, the degrees of freedom of an est |
6,871 | Why is Mantel's test preferred over Moran's I? | Mantel test and Moran's I refer to two very different concepts.
The reason for using Moran's I is the question of spatial autocorrelation: correlation of a variable with itself through space. One uses Moran's I when wants to know to which extent the occurrence of an event in an areal unit makes more likely or unlikel... | Why is Mantel's test preferred over Moran's I? | Mantel test and Moran's I refer to two very different concepts.
The reason for using Moran's I is the question of spatial autocorrelation: correlation of a variable with itself through space. One us | Why is Mantel's test preferred over Moran's I?
Mantel test and Moran's I refer to two very different concepts.
The reason for using Moran's I is the question of spatial autocorrelation: correlation of a variable with itself through space. One uses Moran's I when wants to know to which extent the occurrence of an even... | Why is Mantel's test preferred over Moran's I?
Mantel test and Moran's I refer to two very different concepts.
The reason for using Moran's I is the question of spatial autocorrelation: correlation of a variable with itself through space. One us |
6,872 | Should we address multiple comparisons adjustments when using confidence intervals? | An excellent topic which is, sadly, not given enough attention.
When discussing multiple parameters and confidence intervals, a distinction should be made between simultaneous inference and selective inference. Ref.[2] gives an excellent demonstration of the matter.
Simultaneous confidence intervals mean that all the p... | Should we address multiple comparisons adjustments when using confidence intervals? | An excellent topic which is, sadly, not given enough attention.
When discussing multiple parameters and confidence intervals, a distinction should be made between simultaneous inference and selective | Should we address multiple comparisons adjustments when using confidence intervals?
An excellent topic which is, sadly, not given enough attention.
When discussing multiple parameters and confidence intervals, a distinction should be made between simultaneous inference and selective inference. Ref.[2] gives an excellen... | Should we address multiple comparisons adjustments when using confidence intervals?
An excellent topic which is, sadly, not given enough attention.
When discussing multiple parameters and confidence intervals, a distinction should be made between simultaneous inference and selective |
6,873 | Should we address multiple comparisons adjustments when using confidence intervals? | I would never adjust confidence intervals for multiple testing. I am not a big fan of p-values, because I believe that estimating parameters is a better use of statistics than testing hypotheses which are never exactly true. However I concede that hypothesis testing has its value, in say a randomised controlled trial w... | Should we address multiple comparisons adjustments when using confidence intervals? | I would never adjust confidence intervals for multiple testing. I am not a big fan of p-values, because I believe that estimating parameters is a better use of statistics than testing hypotheses which | Should we address multiple comparisons adjustments when using confidence intervals?
I would never adjust confidence intervals for multiple testing. I am not a big fan of p-values, because I believe that estimating parameters is a better use of statistics than testing hypotheses which are never exactly true. However I c... | Should we address multiple comparisons adjustments when using confidence intervals?
I would never adjust confidence intervals for multiple testing. I am not a big fan of p-values, because I believe that estimating parameters is a better use of statistics than testing hypotheses which |
6,874 | Link Anomaly Detection in Temporal Network | You should first come up with your definition of anomaly-score for a new node(see section 3.1, 3.2). Fortunately, the correspondence between a new post(in their case) and a new node(in your case) is almost one-to-one, since we are only interested in the set of nodes(users) that the node(post) is related to.
Thus, we ca... | Link Anomaly Detection in Temporal Network | You should first come up with your definition of anomaly-score for a new node(see section 3.1, 3.2). Fortunately, the correspondence between a new post(in their case) and a new node(in your case) is a | Link Anomaly Detection in Temporal Network
You should first come up with your definition of anomaly-score for a new node(see section 3.1, 3.2). Fortunately, the correspondence between a new post(in their case) and a new node(in your case) is almost one-to-one, since we are only interested in the set of nodes(users) tha... | Link Anomaly Detection in Temporal Network
You should first come up with your definition of anomaly-score for a new node(see section 3.1, 3.2). Fortunately, the correspondence between a new post(in their case) and a new node(in your case) is a |
6,875 | What is the reason the log transformation is used with right-skewed distributions? | Economists (like me) love the log transformation. We especially love it in regression models, like this:
\begin{align}
\ln{Y_i} &= \beta_1 + \beta_2 \ln{X_i} + \epsilon_i
\end{align}
Why do we love it so much? Here is the list of reasons I give students when I lecture on it:
It respects the positivity of $Y$. Many ... | What is the reason the log transformation is used with right-skewed distributions? | Economists (like me) love the log transformation. We especially love it in regression models, like this:
\begin{align}
\ln{Y_i} &= \beta_1 + \beta_2 \ln{X_i} + \epsilon_i
\end{align}
Why do we love i | What is the reason the log transformation is used with right-skewed distributions?
Economists (like me) love the log transformation. We especially love it in regression models, like this:
\begin{align}
\ln{Y_i} &= \beta_1 + \beta_2 \ln{X_i} + \epsilon_i
\end{align}
Why do we love it so much? Here is the list of reaso... | What is the reason the log transformation is used with right-skewed distributions?
Economists (like me) love the log transformation. We especially love it in regression models, like this:
\begin{align}
\ln{Y_i} &= \beta_1 + \beta_2 \ln{X_i} + \epsilon_i
\end{align}
Why do we love i |
6,876 | What is the reason the log transformation is used with right-skewed distributions? | First let's see what typically happens when we take logs of something that's right skew.
The top row contains histograms for samples from three different, increasingly skewed distributions.
The bottom row contains histograms for their logs.
You can see that the center case ($y$) has been transformed to something close... | What is the reason the log transformation is used with right-skewed distributions? | First let's see what typically happens when we take logs of something that's right skew.
The top row contains histograms for samples from three different, increasingly skewed distributions.
The bottom | What is the reason the log transformation is used with right-skewed distributions?
First let's see what typically happens when we take logs of something that's right skew.
The top row contains histograms for samples from three different, increasingly skewed distributions.
The bottom row contains histograms for their lo... | What is the reason the log transformation is used with right-skewed distributions?
First let's see what typically happens when we take logs of something that's right skew.
The top row contains histograms for samples from three different, increasingly skewed distributions.
The bottom |
6,877 | What is the reason the log transformation is used with right-skewed distributions? | The log function essentially de-emphasizes very large values. Look at the image below which shows $y = ln(x)$. Notice how large values on the $x$-axis are relatively smaller on the y-axis.
Now, in a right-skewed distribution you have a few very large values. The log transformation essentially reels these values into t... | What is the reason the log transformation is used with right-skewed distributions? | The log function essentially de-emphasizes very large values. Look at the image below which shows $y = ln(x)$. Notice how large values on the $x$-axis are relatively smaller on the y-axis.
Now, in a | What is the reason the log transformation is used with right-skewed distributions?
The log function essentially de-emphasizes very large values. Look at the image below which shows $y = ln(x)$. Notice how large values on the $x$-axis are relatively smaller on the y-axis.
Now, in a right-skewed distribution you have a ... | What is the reason the log transformation is used with right-skewed distributions?
The log function essentially de-emphasizes very large values. Look at the image below which shows $y = ln(x)$. Notice how large values on the $x$-axis are relatively smaller on the y-axis.
Now, in a |
6,878 | What is the reason the log transformation is used with right-skewed distributions? | All of these answers are sales pitches for the natural log transformation. There are caveats to its use, caveats that are generalizable to any and all transformations. As a general rule, all mathematical transformations reshape the PDF of the underlying raw variables whether acting to compress, expand, invert, rescale,... | What is the reason the log transformation is used with right-skewed distributions? | All of these answers are sales pitches for the natural log transformation. There are caveats to its use, caveats that are generalizable to any and all transformations. As a general rule, all mathemati | What is the reason the log transformation is used with right-skewed distributions?
All of these answers are sales pitches for the natural log transformation. There are caveats to its use, caveats that are generalizable to any and all transformations. As a general rule, all mathematical transformations reshape the PDF o... | What is the reason the log transformation is used with right-skewed distributions?
All of these answers are sales pitches for the natural log transformation. There are caveats to its use, caveats that are generalizable to any and all transformations. As a general rule, all mathemati |
6,879 | What is the reason the log transformation is used with right-skewed distributions? | Many interesting points have been made. A few more?
1) I would suggest that another issue with linear regression is that the 'left hand side' of the regression equation is E(y) : the expected value. If the error distribution is not symmetrical, then merits for the study of the expected value are weak. The expected valu... | What is the reason the log transformation is used with right-skewed distributions? | Many interesting points have been made. A few more?
1) I would suggest that another issue with linear regression is that the 'left hand side' of the regression equation is E(y) : the expected value. I | What is the reason the log transformation is used with right-skewed distributions?
Many interesting points have been made. A few more?
1) I would suggest that another issue with linear regression is that the 'left hand side' of the regression equation is E(y) : the expected value. If the error distribution is not symme... | What is the reason the log transformation is used with right-skewed distributions?
Many interesting points have been made. A few more?
1) I would suggest that another issue with linear regression is that the 'left hand side' of the regression equation is E(y) : the expected value. I |
6,880 | Is there a good browser/viewer to see an R dataset (.rda file) | Here are a few basic options, but like you, I can't say that I'm entirely happy with my current system.
Avoid using the viewer:
I.e., Use the command line tools to browse the data
head and tail for showing initial and final rows
str for an overview of variable types
dplyr::glimpse() for an overview of variable types ... | Is there a good browser/viewer to see an R dataset (.rda file) | Here are a few basic options, but like you, I can't say that I'm entirely happy with my current system.
Avoid using the viewer:
I.e., Use the command line tools to browse the data
head and tail for s | Is there a good browser/viewer to see an R dataset (.rda file)
Here are a few basic options, but like you, I can't say that I'm entirely happy with my current system.
Avoid using the viewer:
I.e., Use the command line tools to browse the data
head and tail for showing initial and final rows
str for an overview of vari... | Is there a good browser/viewer to see an R dataset (.rda file)
Here are a few basic options, but like you, I can't say that I'm entirely happy with my current system.
Avoid using the viewer:
I.e., Use the command line tools to browse the data
head and tail for s |
6,881 | Is there a good browser/viewer to see an R dataset (.rda file) | I recommend highly the R Package googleVis, R bindings to the Google Visualization API. The Package authors are Markus Gesmann and Diego de Castillo.
The data frame viewer in googleVis is astonishingly simple to use.
These guys have done great work because googleVis is straightforward to use, though the Google Visuali... | Is there a good browser/viewer to see an R dataset (.rda file) | I recommend highly the R Package googleVis, R bindings to the Google Visualization API. The Package authors are Markus Gesmann and Diego de Castillo.
The data frame viewer in googleVis is astonishing | Is there a good browser/viewer to see an R dataset (.rda file)
I recommend highly the R Package googleVis, R bindings to the Google Visualization API. The Package authors are Markus Gesmann and Diego de Castillo.
The data frame viewer in googleVis is astonishingly simple to use.
These guys have done great work because... | Is there a good browser/viewer to see an R dataset (.rda file)
I recommend highly the R Package googleVis, R bindings to the Google Visualization API. The Package authors are Markus Gesmann and Diego de Castillo.
The data frame viewer in googleVis is astonishing |
6,882 | Is there a good browser/viewer to see an R dataset (.rda file) | RStudio (RStudio.org) has a built-in data frame viewer that's pretty good. Luckily it's read-only. RStudio is very easy to install once you've installed a recent version of R. If using Linux first install the r-base package. | Is there a good browser/viewer to see an R dataset (.rda file) | RStudio (RStudio.org) has a built-in data frame viewer that's pretty good. Luckily it's read-only. RStudio is very easy to install once you've installed a recent version of R. If using Linux first | Is there a good browser/viewer to see an R dataset (.rda file)
RStudio (RStudio.org) has a built-in data frame viewer that's pretty good. Luckily it's read-only. RStudio is very easy to install once you've installed a recent version of R. If using Linux first install the r-base package. | Is there a good browser/viewer to see an R dataset (.rda file)
RStudio (RStudio.org) has a built-in data frame viewer that's pretty good. Luckily it's read-only. RStudio is very easy to install once you've installed a recent version of R. If using Linux first |
6,883 | Is there a good browser/viewer to see an R dataset (.rda file) | Here are some other thoughts (although I am always reluctant to leave Emacs):
Deducer (with JGR) allows to view a data.frame with a combined variable/data view (à la SPSS).
J Fox's Rcmdr also offers edit/viewing facilities, although in an X11 environment.
J Verzani's Poor Man Gui (pmg) only allows for quick preview fo... | Is there a good browser/viewer to see an R dataset (.rda file) | Here are some other thoughts (although I am always reluctant to leave Emacs):
Deducer (with JGR) allows to view a data.frame with a combined variable/data view (à la SPSS).
J Fox's Rcmdr also offers | Is there a good browser/viewer to see an R dataset (.rda file)
Here are some other thoughts (although I am always reluctant to leave Emacs):
Deducer (with JGR) allows to view a data.frame with a combined variable/data view (à la SPSS).
J Fox's Rcmdr also offers edit/viewing facilities, although in an X11 environment.
... | Is there a good browser/viewer to see an R dataset (.rda file)
Here are some other thoughts (although I am always reluctant to leave Emacs):
Deducer (with JGR) allows to view a data.frame with a combined variable/data view (à la SPSS).
J Fox's Rcmdr also offers |
6,884 | Is there a good browser/viewer to see an R dataset (.rda file) | You can get View() to display all of your data in RStudio. The trick is that you need to use the command syntax utils::View() instead. (For slightly more information, see my answer on Stack Overflow here: R View() does not display all columns of data frame.) | Is there a good browser/viewer to see an R dataset (.rda file) | You can get View() to display all of your data in RStudio. The trick is that you need to use the command syntax utils::View() instead. (For slightly more information, see my answer on Stack Overflow | Is there a good browser/viewer to see an R dataset (.rda file)
You can get View() to display all of your data in RStudio. The trick is that you need to use the command syntax utils::View() instead. (For slightly more information, see my answer on Stack Overflow here: R View() does not display all columns of data fram... | Is there a good browser/viewer to see an R dataset (.rda file)
You can get View() to display all of your data in RStudio. The trick is that you need to use the command syntax utils::View() instead. (For slightly more information, see my answer on Stack Overflow |
6,885 | Is there a good browser/viewer to see an R dataset (.rda file) | Recently I started to keep the data in a sqlite database, access the database directly from R using sqldf and view / edit with a database tool named tksqlite
Another option is to export the data and view / edit with Google Refine | Is there a good browser/viewer to see an R dataset (.rda file) | Recently I started to keep the data in a sqlite database, access the database directly from R using sqldf and view / edit with a database tool named tksqlite
Another option is to export the data and v | Is there a good browser/viewer to see an R dataset (.rda file)
Recently I started to keep the data in a sqlite database, access the database directly from R using sqldf and view / edit with a database tool named tksqlite
Another option is to export the data and view / edit with Google Refine | Is there a good browser/viewer to see an R dataset (.rda file)
Recently I started to keep the data in a sqlite database, access the database directly from R using sqldf and view / edit with a database tool named tksqlite
Another option is to export the data and v |
6,886 | Is there a good browser/viewer to see an R dataset (.rda file) | The datatable function from DT package creates HTML tables. You can nicely view wide tables. | Is there a good browser/viewer to see an R dataset (.rda file) | The datatable function from DT package creates HTML tables. You can nicely view wide tables. | Is there a good browser/viewer to see an R dataset (.rda file)
The datatable function from DT package creates HTML tables. You can nicely view wide tables. | Is there a good browser/viewer to see an R dataset (.rda file)
The datatable function from DT package creates HTML tables. You can nicely view wide tables. |
6,887 | What does kernel size mean? | Deep neural networks, more concretely convolutional neural networks (CNN), are basically a stack of layers which are defined by the action of a number of filters on the input. Those filters are usually called kernels.
For example, the kernels in the convolutional layer, are the convolutional filters. Actually no convol... | What does kernel size mean? | Deep neural networks, more concretely convolutional neural networks (CNN), are basically a stack of layers which are defined by the action of a number of filters on the input. Those filters are usuall | What does kernel size mean?
Deep neural networks, more concretely convolutional neural networks (CNN), are basically a stack of layers which are defined by the action of a number of filters on the input. Those filters are usually called kernels.
For example, the kernels in the convolutional layer, are the convolutional... | What does kernel size mean?
Deep neural networks, more concretely convolutional neural networks (CNN), are basically a stack of layers which are defined by the action of a number of filters on the input. Those filters are usuall |
6,888 | What does kernel size mean? | As you can see above, the kernel, also known as kernel matrix is the function in between and its size, here 3, is the kernel size(where kernel width equals kernel hight).
Note that the kernel does not necessarily to be symmetric, and we can verify that by quoting this text from the doc of Conv2D in Tensorflow:
kerne... | What does kernel size mean? | As you can see above, the kernel, also known as kernel matrix is the function in between and its size, here 3, is the kernel size(where kernel width equals kernel hight).
Note that the kernel does no | What does kernel size mean?
As you can see above, the kernel, also known as kernel matrix is the function in between and its size, here 3, is the kernel size(where kernel width equals kernel hight).
Note that the kernel does not necessarily to be symmetric, and we can verify that by quoting this text from the doc of C... | What does kernel size mean?
As you can see above, the kernel, also known as kernel matrix is the function in between and its size, here 3, is the kernel size(where kernel width equals kernel hight).
Note that the kernel does no |
6,889 | Why use regularisation in polynomial regression instead of lowering the degree? | I recently made a little in browser app that you can use to play with these ideas: Scatterplot Smoothers (*).
Here's some data I made up, with a low degree polynomial fit
It's clear that the quadratic polynomial is just not flexible enough to give a good fit to the data. We have regions of very high bias, between $0.... | Why use regularisation in polynomial regression instead of lowering the degree? | I recently made a little in browser app that you can use to play with these ideas: Scatterplot Smoothers (*).
Here's some data I made up, with a low degree polynomial fit
It's clear that the quadrati | Why use regularisation in polynomial regression instead of lowering the degree?
I recently made a little in browser app that you can use to play with these ideas: Scatterplot Smoothers (*).
Here's some data I made up, with a low degree polynomial fit
It's clear that the quadratic polynomial is just not flexible enough... | Why use regularisation in polynomial regression instead of lowering the degree?
I recently made a little in browser app that you can use to play with these ideas: Scatterplot Smoothers (*).
Here's some data I made up, with a low degree polynomial fit
It's clear that the quadrati |
6,890 | Why use regularisation in polynomial regression instead of lowering the degree? | No, it isn't the same. Compare, for example, a second-order polynomial without regularization to a fourth-order polynomial with it. The latter can posit big coefficients for the third and fourth powers so long as this seems to increase predictive accuracy, according to whatever procedure is used to choose the penalty s... | Why use regularisation in polynomial regression instead of lowering the degree? | No, it isn't the same. Compare, for example, a second-order polynomial without regularization to a fourth-order polynomial with it. The latter can posit big coefficients for the third and fourth power | Why use regularisation in polynomial regression instead of lowering the degree?
No, it isn't the same. Compare, for example, a second-order polynomial without regularization to a fourth-order polynomial with it. The latter can posit big coefficients for the third and fourth powers so long as this seems to increase pred... | Why use regularisation in polynomial regression instead of lowering the degree?
No, it isn't the same. Compare, for example, a second-order polynomial without regularization to a fourth-order polynomial with it. The latter can posit big coefficients for the third and fourth power |
6,891 | Why use regularisation in polynomial regression instead of lowering the degree? | For polynomials even small changes in coefficients can make a difference for the higher exponents.
$L_2$ regularization ( least squares ) usually encourages many small coefficients but none exactly 0 and therefore the higher order monomials are able to make a difference. | Why use regularisation in polynomial regression instead of lowering the degree? | For polynomials even small changes in coefficients can make a difference for the higher exponents.
$L_2$ regularization ( least squares ) usually encourages many small coefficients but none exactly 0 | Why use regularisation in polynomial regression instead of lowering the degree?
For polynomials even small changes in coefficients can make a difference for the higher exponents.
$L_2$ regularization ( least squares ) usually encourages many small coefficients but none exactly 0 and therefore the higher order monomials... | Why use regularisation in polynomial regression instead of lowering the degree?
For polynomials even small changes in coefficients can make a difference for the higher exponents.
$L_2$ regularization ( least squares ) usually encourages many small coefficients but none exactly 0 |
6,892 | Why use regularisation in polynomial regression instead of lowering the degree? | All the answers are great and I have similar simulations with Matt to give you another example to show why complex model with regularization is usually better than simple model.
I made a analogy to have intuitive explanation.
Case 1 you only have a high school student with limited knowledge (a simple model without reg... | Why use regularisation in polynomial regression instead of lowering the degree? | All the answers are great and I have similar simulations with Matt to give you another example to show why complex model with regularization is usually better than simple model.
I made a analogy to ha | Why use regularisation in polynomial regression instead of lowering the degree?
All the answers are great and I have similar simulations with Matt to give you another example to show why complex model with regularization is usually better than simple model.
I made a analogy to have intuitive explanation.
Case 1 you on... | Why use regularisation in polynomial regression instead of lowering the degree?
All the answers are great and I have similar simulations with Matt to give you another example to show why complex model with regularization is usually better than simple model.
I made a analogy to ha |
6,893 | Why use regularisation in polynomial regression instead of lowering the degree? | Model Complexity (model flexibility) is about representing the structures hidden in the data. To take an example of polynomial curve fitting, a higher-order polynomial (say, parabola/quadratic) provides more flexibility to represent the hidden structures compared to a lower-order one (say, line/linear) if there is inde... | Why use regularisation in polynomial regression instead of lowering the degree? | Model Complexity (model flexibility) is about representing the structures hidden in the data. To take an example of polynomial curve fitting, a higher-order polynomial (say, parabola/quadratic) provid | Why use regularisation in polynomial regression instead of lowering the degree?
Model Complexity (model flexibility) is about representing the structures hidden in the data. To take an example of polynomial curve fitting, a higher-order polynomial (say, parabola/quadratic) provides more flexibility to represent the hid... | Why use regularisation in polynomial regression instead of lowering the degree?
Model Complexity (model flexibility) is about representing the structures hidden in the data. To take an example of polynomial curve fitting, a higher-order polynomial (say, parabola/quadratic) provid |
6,894 | Area under curve of ROC vs. overall accuracy | AUC (based on ROC) and overall accuracy seems not the same concept.
Overall accuracy is based on one specific cutpoint, while ROC tries all of the cutpoint and plots the sensitivity and specificity.
So when we compare the overall accuracy, we are comparing the accuracy based on some cutpoint. The overall accuracy varie... | Area under curve of ROC vs. overall accuracy | AUC (based on ROC) and overall accuracy seems not the same concept.
Overall accuracy is based on one specific cutpoint, while ROC tries all of the cutpoint and plots the sensitivity and specificity.
S | Area under curve of ROC vs. overall accuracy
AUC (based on ROC) and overall accuracy seems not the same concept.
Overall accuracy is based on one specific cutpoint, while ROC tries all of the cutpoint and plots the sensitivity and specificity.
So when we compare the overall accuracy, we are comparing the accuracy based... | Area under curve of ROC vs. overall accuracy
AUC (based on ROC) and overall accuracy seems not the same concept.
Overall accuracy is based on one specific cutpoint, while ROC tries all of the cutpoint and plots the sensitivity and specificity.
S |
6,895 | Area under curve of ROC vs. overall accuracy | While the two statistics measures are likely to be correlated, they measure different qualities of the classifier.
AUROC
The area under the curve (AUC) is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative example. It measures the classifiers... | Area under curve of ROC vs. overall accuracy | While the two statistics measures are likely to be correlated, they measure different qualities of the classifier.
AUROC
The area under the curve (AUC) is equal to the probability that a classifier wi | Area under curve of ROC vs. overall accuracy
While the two statistics measures are likely to be correlated, they measure different qualities of the classifier.
AUROC
The area under the curve (AUC) is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen n... | Area under curve of ROC vs. overall accuracy
While the two statistics measures are likely to be correlated, they measure different qualities of the classifier.
AUROC
The area under the curve (AUC) is equal to the probability that a classifier wi |
6,896 | Area under curve of ROC vs. overall accuracy | Is AUC really very useful metric?
I would say expected cost is more appropriate measure.
Then you would have a cost A for all False Positives and cost B for all False Negatives. It might easily be that other class is relative more expensive than other. Of course if you have costs for false classification in the various... | Area under curve of ROC vs. overall accuracy | Is AUC really very useful metric?
I would say expected cost is more appropriate measure.
Then you would have a cost A for all False Positives and cost B for all False Negatives. It might easily be tha | Area under curve of ROC vs. overall accuracy
Is AUC really very useful metric?
I would say expected cost is more appropriate measure.
Then you would have a cost A for all False Positives and cost B for all False Negatives. It might easily be that other class is relative more expensive than other. Of course if you have ... | Area under curve of ROC vs. overall accuracy
Is AUC really very useful metric?
I would say expected cost is more appropriate measure.
Then you would have a cost A for all False Positives and cost B for all False Negatives. It might easily be tha |
6,897 | Area under curve of ROC vs. overall accuracy | Like all the answers have been posted: ROC and accuracy are fundamentally two different concepts.
Generally speaking, ROC describes the discriminative power of a classifier independent of class distribution and unequal prediction error costs (false positive and false negative cost).
Metric like accuracy is calculated b... | Area under curve of ROC vs. overall accuracy | Like all the answers have been posted: ROC and accuracy are fundamentally two different concepts.
Generally speaking, ROC describes the discriminative power of a classifier independent of class distri | Area under curve of ROC vs. overall accuracy
Like all the answers have been posted: ROC and accuracy are fundamentally two different concepts.
Generally speaking, ROC describes the discriminative power of a classifier independent of class distribution and unequal prediction error costs (false positive and false negativ... | Area under curve of ROC vs. overall accuracy
Like all the answers have been posted: ROC and accuracy are fundamentally two different concepts.
Generally speaking, ROC describes the discriminative power of a classifier independent of class distri |
6,898 | What are the dangers of violating the homoscedasticity assumption for linear regression? | The linear model (or "ordinary least squares") still has its unbiasedness property in this case.
In the face of heteroskedasticity in error terms, you still have unbiased parameter estimates but you get a biased estimate of the covariance matrix: your inference (i.e. parameter tests and confidence intervals) could be ... | What are the dangers of violating the homoscedasticity assumption for linear regression? | The linear model (or "ordinary least squares") still has its unbiasedness property in this case.
In the face of heteroskedasticity in error terms, you still have unbiased parameter estimates but you g | What are the dangers of violating the homoscedasticity assumption for linear regression?
The linear model (or "ordinary least squares") still has its unbiasedness property in this case.
In the face of heteroskedasticity in error terms, you still have unbiased parameter estimates but you get a biased estimate of the co... | What are the dangers of violating the homoscedasticity assumption for linear regression?
The linear model (or "ordinary least squares") still has its unbiasedness property in this case.
In the face of heteroskedasticity in error terms, you still have unbiased parameter estimates but you g |
6,899 | What are the dangers of violating the homoscedasticity assumption for linear regression? | Homoscedasticity is one of the Gauss Markov assumptions that are required for OLS to be the best linear unbiased estimator (BLUE).
The Gauss-Markov Theorem is telling us that the least squares estimator for the coefficients $\beta$ is unbiased and has minimum variance among all unbiased linear estimators, given that w... | What are the dangers of violating the homoscedasticity assumption for linear regression? | Homoscedasticity is one of the Gauss Markov assumptions that are required for OLS to be the best linear unbiased estimator (BLUE).
The Gauss-Markov Theorem is telling us that the least squares estima | What are the dangers of violating the homoscedasticity assumption for linear regression?
Homoscedasticity is one of the Gauss Markov assumptions that are required for OLS to be the best linear unbiased estimator (BLUE).
The Gauss-Markov Theorem is telling us that the least squares estimator for the coefficients $\beta... | What are the dangers of violating the homoscedasticity assumption for linear regression?
Homoscedasticity is one of the Gauss Markov assumptions that are required for OLS to be the best linear unbiased estimator (BLUE).
The Gauss-Markov Theorem is telling us that the least squares estima |
6,900 | What are the dangers of violating the homoscedasticity assumption for linear regression? | Absence of homoscedasticity may give unreliable standard error estimates of the parameters. Parameter estimates are unbiased. But the estimates may not efficient(not BLUE). You can find some more in the following link | What are the dangers of violating the homoscedasticity assumption for linear regression? | Absence of homoscedasticity may give unreliable standard error estimates of the parameters. Parameter estimates are unbiased. But the estimates may not efficient(not BLUE). You can find some more in | What are the dangers of violating the homoscedasticity assumption for linear regression?
Absence of homoscedasticity may give unreliable standard error estimates of the parameters. Parameter estimates are unbiased. But the estimates may not efficient(not BLUE). You can find some more in the following link | What are the dangers of violating the homoscedasticity assumption for linear regression?
Absence of homoscedasticity may give unreliable standard error estimates of the parameters. Parameter estimates are unbiased. But the estimates may not efficient(not BLUE). You can find some more in |
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