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 |
|---|---|---|---|---|---|---|
14,701 | Rand index calculation | I have an implementation of this in R which I will explain:
TP (a in the code) is the sum of every cell choose 2. As per the original question (0 or 1 choose 2 equating to 0)
FN (b) is the sum of each row choose 2, all summed, less TP. Where each Row sum represents the number of documents in each True class.
The sum ... | Rand index calculation | I have an implementation of this in R which I will explain:
TP (a in the code) is the sum of every cell choose 2. As per the original question (0 or 1 choose 2 equating to 0)
FN (b) is the sum of eac | Rand index calculation
I have an implementation of this in R which I will explain:
TP (a in the code) is the sum of every cell choose 2. As per the original question (0 or 1 choose 2 equating to 0)
FN (b) is the sum of each row choose 2, all summed, less TP. Where each Row sum represents the number of documents in eac... | Rand index calculation
I have an implementation of this in R which I will explain:
TP (a in the code) is the sum of every cell choose 2. As per the original question (0 or 1 choose 2 equating to 0)
FN (b) is the sum of eac |
14,702 | Rand index calculation | Below is the picture which describes your question:
To solve this problem, you need to consider this matrix:
+--------------------------------+--------------------------------------+
| TP: | FN: |
| Same class + same cluster | Same class + different clus... | Rand index calculation | Below is the picture which describes your question:
To solve this problem, you need to consider this matrix:
+--------------------------------+--------------------------------------+
| TP: | Rand index calculation
Below is the picture which describes your question:
To solve this problem, you need to consider this matrix:
+--------------------------------+--------------------------------------+
| TP: | FN: |
| Same class + same cluster | Same... | Rand index calculation
Below is the picture which describes your question:
To solve this problem, you need to consider this matrix:
+--------------------------------+--------------------------------------+
| TP: |
14,703 | Rand index calculation | You can compute TN and FN the same way.
Just switch the roles of labels and clusters.
a) 1 1 1 1 1 2 3 3
b) 1 2 2 2 2
c) 2 3 3 3 3
... then perform the same computations. | Rand index calculation | You can compute TN and FN the same way.
Just switch the roles of labels and clusters.
a) 1 1 1 1 1 2 3 3
b) 1 2 2 2 2
c) 2 3 3 3 3
... then perform the same computations. | Rand index calculation
You can compute TN and FN the same way.
Just switch the roles of labels and clusters.
a) 1 1 1 1 1 2 3 3
b) 1 2 2 2 2
c) 2 3 3 3 3
... then perform the same computations. | Rand index calculation
You can compute TN and FN the same way.
Just switch the roles of labels and clusters.
a) 1 1 1 1 1 2 3 3
b) 1 2 2 2 2
c) 2 3 3 3 3
... then perform the same computations. |
14,704 | Rand index calculation | I THINK I've reverse engineered the false negative (FN) out of it. For the true positives, you made 4 groups that were positive. In cluster 1, you had the five a's; in cluster 2, you had the 4 b's; in cluster 3 you had the 3 c's AND the 2 a's.
So for the false negative.
Start with the a's in cluster 1; there are 5 c... | Rand index calculation | I THINK I've reverse engineered the false negative (FN) out of it. For the true positives, you made 4 groups that were positive. In cluster 1, you had the five a's; in cluster 2, you had the 4 b's; in | Rand index calculation
I THINK I've reverse engineered the false negative (FN) out of it. For the true positives, you made 4 groups that were positive. In cluster 1, you had the five a's; in cluster 2, you had the 4 b's; in cluster 3 you had the 3 c's AND the 2 a's.
So for the false negative.
Start with the a's in c... | Rand index calculation
I THINK I've reverse engineered the false negative (FN) out of it. For the true positives, you made 4 groups that were positive. In cluster 1, you had the five a's; in cluster 2, you had the 4 b's; in |
14,705 | Rand index calculation | Here is how to calculate every metric for Rand Index without subtracting
Side notes for easier understanding:
Rand Index is based on comparing pairs of elements. Theory suggests, that similar pairs of elements should be placed in the same cluster, while dissimilar pairs of elements should be placed in separate cluster... | Rand index calculation | Here is how to calculate every metric for Rand Index without subtracting
Side notes for easier understanding:
Rand Index is based on comparing pairs of elements. Theory suggests, that similar pairs o | Rand index calculation
Here is how to calculate every metric for Rand Index without subtracting
Side notes for easier understanding:
Rand Index is based on comparing pairs of elements. Theory suggests, that similar pairs of elements should be placed in the same cluster, while dissimilar pairs of elements should be pla... | Rand index calculation
Here is how to calculate every metric for Rand Index without subtracting
Side notes for easier understanding:
Rand Index is based on comparing pairs of elements. Theory suggests, that similar pairs o |
14,706 | Correlation coefficient between a (non-dichotomous) nominal variable and a numeric (interval) or an ordinal variable | Nominal vs Interval
The most classic "correlation" measure between a nominal and an interval ("numeric") variable is Eta, also called correlation ratio, and equal to the root R-square of the one-way ANOVA (with p-value = that of the ANOVA). Eta can be seen as a symmetric association measure, like correlation, because E... | Correlation coefficient between a (non-dichotomous) nominal variable and a numeric (interval) or an | Nominal vs Interval
The most classic "correlation" measure between a nominal and an interval ("numeric") variable is Eta, also called correlation ratio, and equal to the root R-square of the one-way A | Correlation coefficient between a (non-dichotomous) nominal variable and a numeric (interval) or an ordinal variable
Nominal vs Interval
The most classic "correlation" measure between a nominal and an interval ("numeric") variable is Eta, also called correlation ratio, and equal to the root R-square of the one-way ANOV... | Correlation coefficient between a (non-dichotomous) nominal variable and a numeric (interval) or an
Nominal vs Interval
The most classic "correlation" measure between a nominal and an interval ("numeric") variable is Eta, also called correlation ratio, and equal to the root R-square of the one-way A |
14,707 | Correlation coefficient between a (non-dichotomous) nominal variable and a numeric (interval) or an ordinal variable | Do a one-way anova on the response, with city as the grouping variable. The $F$ and $p$ it gives should be the same as the $F$ and $p$ from the regression of the response on the dummy-coded cities, and $SS_{between\, cities}/SS_{total}$ should equal the multiple $R^2$ from the regression. The multiple $R$ is the correl... | Correlation coefficient between a (non-dichotomous) nominal variable and a numeric (interval) or an | Do a one-way anova on the response, with city as the grouping variable. The $F$ and $p$ it gives should be the same as the $F$ and $p$ from the regression of the response on the dummy-coded cities, an | Correlation coefficient between a (non-dichotomous) nominal variable and a numeric (interval) or an ordinal variable
Do a one-way anova on the response, with city as the grouping variable. The $F$ and $p$ it gives should be the same as the $F$ and $p$ from the regression of the response on the dummy-coded cities, and $... | Correlation coefficient between a (non-dichotomous) nominal variable and a numeric (interval) or an
Do a one-way anova on the response, with city as the grouping variable. The $F$ and $p$ it gives should be the same as the $F$ and $p$ from the regression of the response on the dummy-coded cities, an |
14,708 | What is the optimal distance function for individuals when attributes are nominal? | Technically to compute a dis(similarity) measure between individuals on nominal attributes most programs first recode each nominal variable into a set of dummy binary variables and then compute some measure for binary variables. Here is formulas of some frequently used binary similarity and dissimilarity measures.
What... | What is the optimal distance function for individuals when attributes are nominal? | Technically to compute a dis(similarity) measure between individuals on nominal attributes most programs first recode each nominal variable into a set of dummy binary variables and then compute some m | What is the optimal distance function for individuals when attributes are nominal?
Technically to compute a dis(similarity) measure between individuals on nominal attributes most programs first recode each nominal variable into a set of dummy binary variables and then compute some measure for binary variables. Here is ... | What is the optimal distance function for individuals when attributes are nominal?
Technically to compute a dis(similarity) measure between individuals on nominal attributes most programs first recode each nominal variable into a set of dummy binary variables and then compute some m |
14,709 | How to do regression with effect coding instead of dummy coding in R? | In principle, there are two types of contrast coding, with which the intercept will estimate the Grand Mean. These are sum contrasts and repeated contrasts (sliding differences).
Here's an example data set:
set.seed(42)
x <- data.frame(a = c(rnorm(100,2), rnorm(100,1),rnorm(100,0)),
b = rep(c("A", "B", ... | How to do regression with effect coding instead of dummy coding in R? | In principle, there are two types of contrast coding, with which the intercept will estimate the Grand Mean. These are sum contrasts and repeated contrasts (sliding differences).
Here's an example dat | How to do regression with effect coding instead of dummy coding in R?
In principle, there are two types of contrast coding, with which the intercept will estimate the Grand Mean. These are sum contrasts and repeated contrasts (sliding differences).
Here's an example data set:
set.seed(42)
x <- data.frame(a = c(rnorm(10... | How to do regression with effect coding instead of dummy coding in R?
In principle, there are two types of contrast coding, with which the intercept will estimate the Grand Mean. These are sum contrasts and repeated contrasts (sliding differences).
Here's an example dat |
14,710 | How to do regression with effect coding instead of dummy coding in R? | Nitpicking: if your professor told you to code your variables with (-1, 1), he told you to use effect coding, not effect sizes. At any rate, @user20650 is right. As usual, the UCLA stats help website has a useful page the explains how to do this with R. | How to do regression with effect coding instead of dummy coding in R? | Nitpicking: if your professor told you to code your variables with (-1, 1), he told you to use effect coding, not effect sizes. At any rate, @user20650 is right. As usual, the UCLA stats help websit | How to do regression with effect coding instead of dummy coding in R?
Nitpicking: if your professor told you to code your variables with (-1, 1), he told you to use effect coding, not effect sizes. At any rate, @user20650 is right. As usual, the UCLA stats help website has a useful page the explains how to do this wi... | How to do regression with effect coding instead of dummy coding in R?
Nitpicking: if your professor told you to code your variables with (-1, 1), he told you to use effect coding, not effect sizes. At any rate, @user20650 is right. As usual, the UCLA stats help websit |
14,711 | Difference time series before Arima or within Arima | There are several issues here.
If you difference first, then Arima() will fit a model to the differenced data. If you let Arima() do the differencing as part of the estimation procedure, it will use a diffuse prior for the initialization. This is explained in the help file for arima(). So the results will be different... | Difference time series before Arima or within Arima | There are several issues here.
If you difference first, then Arima() will fit a model to the differenced data. If you let Arima() do the differencing as part of the estimation procedure, it will use | Difference time series before Arima or within Arima
There are several issues here.
If you difference first, then Arima() will fit a model to the differenced data. If you let Arima() do the differencing as part of the estimation procedure, it will use a diffuse prior for the initialization. This is explained in the hel... | Difference time series before Arima or within Arima
There are several issues here.
If you difference first, then Arima() will fit a model to the differenced data. If you let Arima() do the differencing as part of the estimation procedure, it will use |
14,712 | Difference time series before Arima or within Arima | Sometimes you need to remove local means to make the series stationary. If the original series has an acf that doesn't die out this can be due to a level/step shift in the series. The remedy is to de-mean the series.
RESPONSE TO BOUNTY:
The way to get the same results/fitted values is after physically differencing the ... | Difference time series before Arima or within Arima | Sometimes you need to remove local means to make the series stationary. If the original series has an acf that doesn't die out this can be due to a level/step shift in the series. The remedy is to de- | Difference time series before Arima or within Arima
Sometimes you need to remove local means to make the series stationary. If the original series has an acf that doesn't die out this can be due to a level/step shift in the series. The remedy is to de-mean the series.
RESPONSE TO BOUNTY:
The way to get the same results... | Difference time series before Arima or within Arima
Sometimes you need to remove local means to make the series stationary. If the original series has an acf that doesn't die out this can be due to a level/step shift in the series. The remedy is to de- |
14,713 | Difference time series before Arima or within Arima | I don't know why there would be a difference in the results unless somehow you are differencing more times one way than the other. for an ARIMA(p,d,q) the d differences are done first before any model fitting. Then the stationary ARMA(p,q) model is fit to the differenced series. The assumption is that after the rem... | Difference time series before Arima or within Arima | I don't know why there would be a difference in the results unless somehow you are differencing more times one way than the other. for an ARIMA(p,d,q) the d differences are done first before any mod | Difference time series before Arima or within Arima
I don't know why there would be a difference in the results unless somehow you are differencing more times one way than the other. for an ARIMA(p,d,q) the d differences are done first before any model fitting. Then the stationary ARMA(p,q) model is fit to the diffe... | Difference time series before Arima or within Arima
I don't know why there would be a difference in the results unless somehow you are differencing more times one way than the other. for an ARIMA(p,d,q) the d differences are done first before any mod |
14,714 | Difference time series before Arima or within Arima | One reason to difference an I(1) series is to make it stationary. Presuming you have the correct specification for the ARIMA model, the residuals to the model will have the autoregressive and moving average components removed and should be stationary. In that respect it can make sense to use the residuals to the model,... | Difference time series before Arima or within Arima | One reason to difference an I(1) series is to make it stationary. Presuming you have the correct specification for the ARIMA model, the residuals to the model will have the autoregressive and moving a | Difference time series before Arima or within Arima
One reason to difference an I(1) series is to make it stationary. Presuming you have the correct specification for the ARIMA model, the residuals to the model will have the autoregressive and moving average components removed and should be stationary. In that respect ... | Difference time series before Arima or within Arima
One reason to difference an I(1) series is to make it stationary. Presuming you have the correct specification for the ARIMA model, the residuals to the model will have the autoregressive and moving a |
14,715 | Large scale text classification | This should be possible to make it work as long as the data is represented as a sparse data structure such as scipy.sparse.csr_matrix instance in Python. I wrote a tutorial for working on text data. It is further possible to reduce the memory usage further by leveraging the hashing trick: adapt it to use the HashingVec... | Large scale text classification | This should be possible to make it work as long as the data is represented as a sparse data structure such as scipy.sparse.csr_matrix instance in Python. I wrote a tutorial for working on text data. I | Large scale text classification
This should be possible to make it work as long as the data is represented as a sparse data structure such as scipy.sparse.csr_matrix instance in Python. I wrote a tutorial for working on text data. It is further possible to reduce the memory usage further by leveraging the hashing trick... | Large scale text classification
This should be possible to make it work as long as the data is represented as a sparse data structure such as scipy.sparse.csr_matrix instance in Python. I wrote a tutorial for working on text data. I |
14,716 | Large scale text classification | Gensim for Python is magic. And since it's in Python, you can use it in conjunction with @ogrisel's suggestion. | Large scale text classification | Gensim for Python is magic. And since it's in Python, you can use it in conjunction with @ogrisel's suggestion. | Large scale text classification
Gensim for Python is magic. And since it's in Python, you can use it in conjunction with @ogrisel's suggestion. | Large scale text classification
Gensim for Python is magic. And since it's in Python, you can use it in conjunction with @ogrisel's suggestion. |
14,717 | Large scale text classification | Not to toot my own horn, but I made a pretty popular video series on text analytics with Rapidminer. You can see it here:
http://vancouverdata.blogspot.com/2010/11/text-analytics-with-rapidminer-loading.html
You can likely avoid doing feature selection, just use a classifier that doesn't create a million * million matr... | Large scale text classification | Not to toot my own horn, but I made a pretty popular video series on text analytics with Rapidminer. You can see it here:
http://vancouverdata.blogspot.com/2010/11/text-analytics-with-rapidminer-loadi | Large scale text classification
Not to toot my own horn, but I made a pretty popular video series on text analytics with Rapidminer. You can see it here:
http://vancouverdata.blogspot.com/2010/11/text-analytics-with-rapidminer-loading.html
You can likely avoid doing feature selection, just use a classifier that doesn't... | Large scale text classification
Not to toot my own horn, but I made a pretty popular video series on text analytics with Rapidminer. You can see it here:
http://vancouverdata.blogspot.com/2010/11/text-analytics-with-rapidminer-loadi |
14,718 | Large scale text classification | First, based on your comments, I would treat this as 300 binary (yes/no) classification problems. There are many easy-to-use open source binary classifier learners, and this lets you trade time for memory.
SVMs and logistic regression are probably the most popular approaches for text classification. Both can easil... | Large scale text classification | First, based on your comments, I would treat this as 300 binary (yes/no) classification problems. There are many easy-to-use open source binary classifier learners, and this lets you trade time for m | Large scale text classification
First, based on your comments, I would treat this as 300 binary (yes/no) classification problems. There are many easy-to-use open source binary classifier learners, and this lets you trade time for memory.
SVMs and logistic regression are probably the most popular approaches for text... | Large scale text classification
First, based on your comments, I would treat this as 300 binary (yes/no) classification problems. There are many easy-to-use open source binary classifier learners, and this lets you trade time for m |
14,719 | Large scale text classification | Since Sklearn 0.13 there is indeed an implementation of the HashingVectorizer.
EDIT: Here is a full-fledged example of such an application from sklearn docs
Basically, this example demonstrates that you can classify text on data that cannot fit in the computer's main memory (but rather on disk / network / ...). | Large scale text classification | Since Sklearn 0.13 there is indeed an implementation of the HashingVectorizer.
EDIT: Here is a full-fledged example of such an application from sklearn docs
Basically, this example demonstrates that y | Large scale text classification
Since Sklearn 0.13 there is indeed an implementation of the HashingVectorizer.
EDIT: Here is a full-fledged example of such an application from sklearn docs
Basically, this example demonstrates that you can classify text on data that cannot fit in the computer's main memory (but rather o... | Large scale text classification
Since Sklearn 0.13 there is indeed an implementation of the HashingVectorizer.
EDIT: Here is a full-fledged example of such an application from sklearn docs
Basically, this example demonstrates that y |
14,720 | Generate uniformly distributed weights that sum to unity? | Choose $\mathbf{x} \in [0,1]^{n-1}$ uniformly (by means of $n-1$ uniform reals in the interval $[0,1]$). Sort the coefficients so that $0 \le x_1 \le \cdots \le x_{n-1}$. Set
$$\mathbf{w} = (x_1, x_2-x_1, x_3 - x_2, \ldots, x_{n-1} - x_{n-2}, 1 - x_{n-1}).$$
Because we can recover the sorted $x_i$ by means of the pa... | Generate uniformly distributed weights that sum to unity? | Choose $\mathbf{x} \in [0,1]^{n-1}$ uniformly (by means of $n-1$ uniform reals in the interval $[0,1]$). Sort the coefficients so that $0 \le x_1 \le \cdots \le x_{n-1}$. Set
$$\mathbf{w} = (x_1, x | Generate uniformly distributed weights that sum to unity?
Choose $\mathbf{x} \in [0,1]^{n-1}$ uniformly (by means of $n-1$ uniform reals in the interval $[0,1]$). Sort the coefficients so that $0 \le x_1 \le \cdots \le x_{n-1}$. Set
$$\mathbf{w} = (x_1, x_2-x_1, x_3 - x_2, \ldots, x_{n-1} - x_{n-2}, 1 - x_{n-1}).$$
... | Generate uniformly distributed weights that sum to unity?
Choose $\mathbf{x} \in [0,1]^{n-1}$ uniformly (by means of $n-1$ uniform reals in the interval $[0,1]$). Sort the coefficients so that $0 \le x_1 \le \cdots \le x_{n-1}$. Set
$$\mathbf{w} = (x_1, x |
14,721 | Generate uniformly distributed weights that sum to unity? | zz <- c(0, log(-log(runif(n-1))))
ezz <- exp(zz)
w <- ezz/sum(ezz)
The first entry is put to zero for identification; you would see that done in multinomial logistic models. Of course, in multinomial models, you would also have covariates under the exponents, rather than just the random zzs. The distribution o... | Generate uniformly distributed weights that sum to unity? | zz <- c(0, log(-log(runif(n-1))))
ezz <- exp(zz)
w <- ezz/sum(ezz)
The first entry is put to zero for identification; you would see that done in multinomial logistic models. Of course, in mul | Generate uniformly distributed weights that sum to unity?
zz <- c(0, log(-log(runif(n-1))))
ezz <- exp(zz)
w <- ezz/sum(ezz)
The first entry is put to zero for identification; you would see that done in multinomial logistic models. Of course, in multinomial models, you would also have covariates under the expo... | Generate uniformly distributed weights that sum to unity?
zz <- c(0, log(-log(runif(n-1))))
ezz <- exp(zz)
w <- ezz/sum(ezz)
The first entry is put to zero for identification; you would see that done in multinomial logistic models. Of course, in mul |
14,722 | Generate uniformly distributed weights that sum to unity? | The solution is obvious. The following MathLab code provides the answer for 3 weights.
function [ ] = TESTGEN( )
SZ = 1000;
V = zeros (1, 3);
VS = zeros (SZ, 3);
for NIT=1:SZ
V(1) = rand (1,1); % uniform generation on the range 0..1
V(2) = rand (1,1) * (1 - V(1));
V(3) = 1 - V(1) - V(2);
PERM = ... | Generate uniformly distributed weights that sum to unity? | The solution is obvious. The following MathLab code provides the answer for 3 weights.
function [ ] = TESTGEN( )
SZ = 1000;
V = zeros (1, 3);
VS = zeros (SZ, 3);
for NIT=1:SZ
V(1) = rand (1,1 | Generate uniformly distributed weights that sum to unity?
The solution is obvious. The following MathLab code provides the answer for 3 weights.
function [ ] = TESTGEN( )
SZ = 1000;
V = zeros (1, 3);
VS = zeros (SZ, 3);
for NIT=1:SZ
V(1) = rand (1,1); % uniform generation on the range 0..1
V(2) = rand (... | Generate uniformly distributed weights that sum to unity?
The solution is obvious. The following MathLab code provides the answer for 3 weights.
function [ ] = TESTGEN( )
SZ = 1000;
V = zeros (1, 3);
VS = zeros (SZ, 3);
for NIT=1:SZ
V(1) = rand (1,1 |
14,723 | Resources for learning to create data visualizations? | Flowing data regularly discusses the tools that he uses. See, for instance:
40 Essential Tools and Resources to Visualize Data
What Visualization Tool/Software Should You Use? – Getting Started
He also shows in great detail how he makes graphics on occasion, such as:
How to Make a US County Thematic Map Using Free ... | Resources for learning to create data visualizations? | Flowing data regularly discusses the tools that he uses. See, for instance:
40 Essential Tools and Resources to Visualize Data
What Visualization Tool/Software Should You Use? – Getting Started
He | Resources for learning to create data visualizations?
Flowing data regularly discusses the tools that he uses. See, for instance:
40 Essential Tools and Resources to Visualize Data
What Visualization Tool/Software Should You Use? – Getting Started
He also shows in great detail how he makes graphics on occasion, such... | Resources for learning to create data visualizations?
Flowing data regularly discusses the tools that he uses. See, for instance:
40 Essential Tools and Resources to Visualize Data
What Visualization Tool/Software Should You Use? – Getting Started
He |
14,724 | Resources for learning to create data visualizations? | Already mentioned processing has a nice set of books available. See: 1, 2, 3, 4, 5, 6, 7
You will find lots of stuff on the web to help you start with R. As next step then ggplot2 has excellent web documentation. I also found Hadley's book very helpful.
Python might be another way to go. Especially with tools like:
ma... | Resources for learning to create data visualizations? | Already mentioned processing has a nice set of books available. See: 1, 2, 3, 4, 5, 6, 7
You will find lots of stuff on the web to help you start with R. As next step then ggplot2 has excellent web do | Resources for learning to create data visualizations?
Already mentioned processing has a nice set of books available. See: 1, 2, 3, 4, 5, 6, 7
You will find lots of stuff on the web to help you start with R. As next step then ggplot2 has excellent web documentation. I also found Hadley's book very helpful.
Python might... | Resources for learning to create data visualizations?
Already mentioned processing has a nice set of books available. See: 1, 2, 3, 4, 5, 6, 7
You will find lots of stuff on the web to help you start with R. As next step then ggplot2 has excellent web do |
14,725 | Resources for learning to create data visualizations? | You'll spend a lot of time getting up to speed with R.
RapidMiner is free and open source and graphical, and has plenty of good visualizations, and you can export them.
If you have money to spare, or are a university staff/student then JMP is also very freaking nice. It can make some very pretty graphs, very very easi... | Resources for learning to create data visualizations? | You'll spend a lot of time getting up to speed with R.
RapidMiner is free and open source and graphical, and has plenty of good visualizations, and you can export them.
If you have money to spare, or | Resources for learning to create data visualizations?
You'll spend a lot of time getting up to speed with R.
RapidMiner is free and open source and graphical, and has plenty of good visualizations, and you can export them.
If you have money to spare, or are a university staff/student then JMP is also very freaking nic... | Resources for learning to create data visualizations?
You'll spend a lot of time getting up to speed with R.
RapidMiner is free and open source and graphical, and has plenty of good visualizations, and you can export them.
If you have money to spare, or |
14,726 | Resources for learning to create data visualizations? | Another good alternative is the protovis library http://vis.stanford.edu/protovis/
It is a very well crafted JavaScript library that can create some beautiful visualizations if you have the time and ability to write the modest amount of JavaScript code needed.
I also highly recommend Tableau http://www.tableausoftware.... | Resources for learning to create data visualizations? | Another good alternative is the protovis library http://vis.stanford.edu/protovis/
It is a very well crafted JavaScript library that can create some beautiful visualizations if you have the time and a | Resources for learning to create data visualizations?
Another good alternative is the protovis library http://vis.stanford.edu/protovis/
It is a very well crafted JavaScript library that can create some beautiful visualizations if you have the time and ability to write the modest amount of JavaScript code needed.
I als... | Resources for learning to create data visualizations?
Another good alternative is the protovis library http://vis.stanford.edu/protovis/
It is a very well crafted JavaScript library that can create some beautiful visualizations if you have the time and a |
14,727 | Resources for learning to create data visualizations? | Many excellent answers have been given here, and the languages/libraries you choose to learn will be dependent on the type of visualization you would like to do.
However, if you use Python regularly then I highly recommend seaborn. It is very sophisticated when it comes to statistical data visualization, but also looks... | Resources for learning to create data visualizations? | Many excellent answers have been given here, and the languages/libraries you choose to learn will be dependent on the type of visualization you would like to do.
However, if you use Python regularly t | Resources for learning to create data visualizations?
Many excellent answers have been given here, and the languages/libraries you choose to learn will be dependent on the type of visualization you would like to do.
However, if you use Python regularly then I highly recommend seaborn. It is very sophisticated when it c... | Resources for learning to create data visualizations?
Many excellent answers have been given here, and the languages/libraries you choose to learn will be dependent on the type of visualization you would like to do.
However, if you use Python regularly t |
14,728 | Resources for learning to create data visualizations? | Here is a good set of links with resources for starting to learn:
http://blog.cartodb.com/learning-data-visualization | Resources for learning to create data visualizations? | Here is a good set of links with resources for starting to learn:
http://blog.cartodb.com/learning-data-visualization | Resources for learning to create data visualizations?
Here is a good set of links with resources for starting to learn:
http://blog.cartodb.com/learning-data-visualization | Resources for learning to create data visualizations?
Here is a good set of links with resources for starting to learn:
http://blog.cartodb.com/learning-data-visualization |
14,729 | Resources for learning to create data visualizations? | R is great, but it is not that R is difficult to learn it's that the documentation is impossible to search for any other name like Rq would be great. So when you got a problem, searching for a solution is a nightmare, and the documentation is not great either. Matlab or Octave will be great. And to get those plots in R... | Resources for learning to create data visualizations? | R is great, but it is not that R is difficult to learn it's that the documentation is impossible to search for any other name like Rq would be great. So when you got a problem, searching for a solutio | Resources for learning to create data visualizations?
R is great, but it is not that R is difficult to learn it's that the documentation is impossible to search for any other name like Rq would be great. So when you got a problem, searching for a solution is a nightmare, and the documentation is not great either. Matla... | Resources for learning to create data visualizations?
R is great, but it is not that R is difficult to learn it's that the documentation is impossible to search for any other name like Rq would be great. So when you got a problem, searching for a solutio |
14,730 | Resources for learning to create data visualizations? | Here's a YouTube tutorial on D3.js that teaches the basics of HTML, SVG, CSS and JavaScript, as well as how to load data and create a bar chart, line chart, and scatter plot with D3.js. | Resources for learning to create data visualizations? | Here's a YouTube tutorial on D3.js that teaches the basics of HTML, SVG, CSS and JavaScript, as well as how to load data and create a bar chart, line chart, and scatter plot with D3.js. | Resources for learning to create data visualizations?
Here's a YouTube tutorial on D3.js that teaches the basics of HTML, SVG, CSS and JavaScript, as well as how to load data and create a bar chart, line chart, and scatter plot with D3.js. | Resources for learning to create data visualizations?
Here's a YouTube tutorial on D3.js that teaches the basics of HTML, SVG, CSS and JavaScript, as well as how to load data and create a bar chart, line chart, and scatter plot with D3.js. |
14,731 | Resources for learning to create data visualizations? | here's a practical resource to get you started with d3. It includes a demo code and a step-by-step example on how to load, organize and visualize a dataset in d3.
https://www.edx.org/course/web-app-development-with-the-power-of-nodejs | Resources for learning to create data visualizations? | here's a practical resource to get you started with d3. It includes a demo code and a step-by-step example on how to load, organize and visualize a dataset in d3.
https://www.edx.org/course/web-app-d | Resources for learning to create data visualizations?
here's a practical resource to get you started with d3. It includes a demo code and a step-by-step example on how to load, organize and visualize a dataset in d3.
https://www.edx.org/course/web-app-development-with-the-power-of-nodejs | Resources for learning to create data visualizations?
here's a practical resource to get you started with d3. It includes a demo code and a step-by-step example on how to load, organize and visualize a dataset in d3.
https://www.edx.org/course/web-app-d |
14,732 | Resources for learning to create data visualizations? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
There are infinite resources, but you can narrow them ... | Resources for learning to create data visualizations? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| Resources for learning to create data visualizations?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
... | Resources for learning to create data visualizations?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
14,733 | Why does using pseudo-labeling non-trivially affect the results? | Pseudo-labeling doesn't work on the given toy problem
Oliver et al. (2018) evaluated different semi-supervised learning algorithms. Their first figure shows how pseudo-labeling (and other methods) perform on the same toy problem as in your question (called the 'two-moons' dataset):
The plot shows the labeled and unlab... | Why does using pseudo-labeling non-trivially affect the results? | Pseudo-labeling doesn't work on the given toy problem
Oliver et al. (2018) evaluated different semi-supervised learning algorithms. Their first figure shows how pseudo-labeling (and other methods) per | Why does using pseudo-labeling non-trivially affect the results?
Pseudo-labeling doesn't work on the given toy problem
Oliver et al. (2018) evaluated different semi-supervised learning algorithms. Their first figure shows how pseudo-labeling (and other methods) perform on the same toy problem as in your question (calle... | Why does using pseudo-labeling non-trivially affect the results?
Pseudo-labeling doesn't work on the given toy problem
Oliver et al. (2018) evaluated different semi-supervised learning algorithms. Their first figure shows how pseudo-labeling (and other methods) per |
14,734 | Why does using pseudo-labeling non-trivially affect the results? | What you may be overlooking in how self-training works is that:
It's iterative, not one-shot.
You use a classifier that returns probabilistic values. At each iteration, you only add psuedo-labels for the cases your algorithm is most certain about.
In your example, perhaps the first iteration is only confident enough ... | Why does using pseudo-labeling non-trivially affect the results? | What you may be overlooking in how self-training works is that:
It's iterative, not one-shot.
You use a classifier that returns probabilistic values. At each iteration, you only add psuedo-labels for | Why does using pseudo-labeling non-trivially affect the results?
What you may be overlooking in how self-training works is that:
It's iterative, not one-shot.
You use a classifier that returns probabilistic values. At each iteration, you only add psuedo-labels for the cases your algorithm is most certain about.
In yo... | Why does using pseudo-labeling non-trivially affect the results?
What you may be overlooking in how self-training works is that:
It's iterative, not one-shot.
You use a classifier that returns probabilistic values. At each iteration, you only add psuedo-labels for |
14,735 | Why does using pseudo-labeling non-trivially affect the results? | Warning, I am not an expert on this procedure. My failure to produce good results is not proof that the technique cannot be made to work. Furthermore, your image has the general description of "semi-supervised" learning, which is a broad area with a variety of techniques.
I agree with your intuition, I'm not seeing how... | Why does using pseudo-labeling non-trivially affect the results? | Warning, I am not an expert on this procedure. My failure to produce good results is not proof that the technique cannot be made to work. Furthermore, your image has the general description of "semi-s | Why does using pseudo-labeling non-trivially affect the results?
Warning, I am not an expert on this procedure. My failure to produce good results is not proof that the technique cannot be made to work. Furthermore, your image has the general description of "semi-supervised" learning, which is a broad area with a varie... | Why does using pseudo-labeling non-trivially affect the results?
Warning, I am not an expert on this procedure. My failure to produce good results is not proof that the technique cannot be made to work. Furthermore, your image has the general description of "semi-s |
14,736 | Why does using pseudo-labeling non-trivially affect the results? | Here is my guess (I do not know much about this topic either, just wanted to add my two cents to this discussion).
I think that you're right, there's no point in training a classical model and using its predictions as data, because as you say, there's no incentive to the optimiser to do any better. I would guess that r... | Why does using pseudo-labeling non-trivially affect the results? | Here is my guess (I do not know much about this topic either, just wanted to add my two cents to this discussion).
I think that you're right, there's no point in training a classical model and using i | Why does using pseudo-labeling non-trivially affect the results?
Here is my guess (I do not know much about this topic either, just wanted to add my two cents to this discussion).
I think that you're right, there's no point in training a classical model and using its predictions as data, because as you say, there's no ... | Why does using pseudo-labeling non-trivially affect the results?
Here is my guess (I do not know much about this topic either, just wanted to add my two cents to this discussion).
I think that you're right, there's no point in training a classical model and using i |
14,737 | Why do temporal difference (TD) methods have lower variance than Monte Carlo methods? | The difference between the algorithms, is how they set a new value target based on experience.
Using action values to make it a little more concrete, and sticking with on-policy evaluation (not control) to keep arguments simple, then the update to estimate $Q(S_t,A_t)$ takes the same general form for both TD and Monte ... | Why do temporal difference (TD) methods have lower variance than Monte Carlo methods? | The difference between the algorithms, is how they set a new value target based on experience.
Using action values to make it a little more concrete, and sticking with on-policy evaluation (not contro | Why do temporal difference (TD) methods have lower variance than Monte Carlo methods?
The difference between the algorithms, is how they set a new value target based on experience.
Using action values to make it a little more concrete, and sticking with on-policy evaluation (not control) to keep arguments simple, then ... | Why do temporal difference (TD) methods have lower variance than Monte Carlo methods?
The difference between the algorithms, is how they set a new value target based on experience.
Using action values to make it a little more concrete, and sticking with on-policy evaluation (not contro |
14,738 | Biased Data in Machine Learning | You are right to be concerned - even the best models can fail spectacularly if the distribution of out-of-sample data differs significantly from the distribution of the data that the model was trained/tested on.
I think the best you can do is train a model on the labelled data that you have, but try to keep the model i... | Biased Data in Machine Learning | You are right to be concerned - even the best models can fail spectacularly if the distribution of out-of-sample data differs significantly from the distribution of the data that the model was trained | Biased Data in Machine Learning
You are right to be concerned - even the best models can fail spectacularly if the distribution of out-of-sample data differs significantly from the distribution of the data that the model was trained/tested on.
I think the best you can do is train a model on the labelled data that you h... | Biased Data in Machine Learning
You are right to be concerned - even the best models can fail spectacularly if the distribution of out-of-sample data differs significantly from the distribution of the data that the model was trained |
14,739 | Biased Data in Machine Learning | I'm not sure I entirely understand that question, but so far as I understand it you're asking how to train a classifier to predict on samples lying outside the domain of the samples it has already seen. This is, generally speaking and so far as I know, not possible. Machine learning theory is based on the idea of "empi... | Biased Data in Machine Learning | I'm not sure I entirely understand that question, but so far as I understand it you're asking how to train a classifier to predict on samples lying outside the domain of the samples it has already see | Biased Data in Machine Learning
I'm not sure I entirely understand that question, but so far as I understand it you're asking how to train a classifier to predict on samples lying outside the domain of the samples it has already seen. This is, generally speaking and so far as I know, not possible. Machine learning theo... | Biased Data in Machine Learning
I'm not sure I entirely understand that question, but so far as I understand it you're asking how to train a classifier to predict on samples lying outside the domain of the samples it has already see |
14,740 | Biased Data in Machine Learning | Your rules may give you a way to perform data augmentation. Copy a positive sample, change the age to 17, and then mark it as a negative sample.
This procedure won't necessarily be trivial or useful for all datasets. I work with NLP data and it's tricky to do well in that domain. For example, if you have other featu... | Biased Data in Machine Learning | Your rules may give you a way to perform data augmentation. Copy a positive sample, change the age to 17, and then mark it as a negative sample.
This procedure won't necessarily be trivial or useful | Biased Data in Machine Learning
Your rules may give you a way to perform data augmentation. Copy a positive sample, change the age to 17, and then mark it as a negative sample.
This procedure won't necessarily be trivial or useful for all datasets. I work with NLP data and it's tricky to do well in that domain. For ... | Biased Data in Machine Learning
Your rules may give you a way to perform data augmentation. Copy a positive sample, change the age to 17, and then mark it as a negative sample.
This procedure won't necessarily be trivial or useful |
14,741 | Biased Data in Machine Learning | One thing that has worked for us in a similar situation is doing a bit of reinforcement learning (explore and exploit). On top of the rule based model, we ran a explorer which would with a small likelihood change the response of the model, so in occasional cases where the model would not recommend a card to a 17-year o... | Biased Data in Machine Learning | One thing that has worked for us in a similar situation is doing a bit of reinforcement learning (explore and exploit). On top of the rule based model, we ran a explorer which would with a small likel | Biased Data in Machine Learning
One thing that has worked for us in a similar situation is doing a bit of reinforcement learning (explore and exploit). On top of the rule based model, we ran a explorer which would with a small likelihood change the response of the model, so in occasional cases where the model would not... | Biased Data in Machine Learning
One thing that has worked for us in a similar situation is doing a bit of reinforcement learning (explore and exploit). On top of the rule based model, we ran a explorer which would with a small likel |
14,742 | Biased Data in Machine Learning | From a practical standpoint it is difficult/unreasonable to ask a model to predict something on cases that are not possible in the current system (no free lunch).
One way to circumvent that problem is to add randomization to the current (deployed) system, e.g. to add the possibility to bypass (some of) the rules with a... | Biased Data in Machine Learning | From a practical standpoint it is difficult/unreasonable to ask a model to predict something on cases that are not possible in the current system (no free lunch).
One way to circumvent that problem is | Biased Data in Machine Learning
From a practical standpoint it is difficult/unreasonable to ask a model to predict something on cases that are not possible in the current system (no free lunch).
One way to circumvent that problem is to add randomization to the current (deployed) system, e.g. to add the possibility to b... | Biased Data in Machine Learning
From a practical standpoint it is difficult/unreasonable to ask a model to predict something on cases that are not possible in the current system (no free lunch).
One way to circumvent that problem is |
14,743 | Biased Data in Machine Learning | The classical statistical answer is that if the selection process is in the data and described by the model or selection is at random then the parametrical model contemplates it correctly. See Donald Rubin paper Inference and Missing data (1976). You do need to include the mechanism of data selection in your model. Th... | Biased Data in Machine Learning | The classical statistical answer is that if the selection process is in the data and described by the model or selection is at random then the parametrical model contemplates it correctly. See Donald | Biased Data in Machine Learning
The classical statistical answer is that if the selection process is in the data and described by the model or selection is at random then the parametrical model contemplates it correctly. See Donald Rubin paper Inference and Missing data (1976). You do need to include the mechanism of ... | Biased Data in Machine Learning
The classical statistical answer is that if the selection process is in the data and described by the model or selection is at random then the parametrical model contemplates it correctly. See Donald |
14,744 | Biased Data in Machine Learning | This is akin to the after-life dilemma: what ratio of good and bad deeds (data) is sufficient to get to heaven instead of hell (class), after one dies (filter!). Herein, death serves as the filter, leading to missing values towards a supervised learning scheme.
I want to disambiguate between missing-value problem and '... | Biased Data in Machine Learning | This is akin to the after-life dilemma: what ratio of good and bad deeds (data) is sufficient to get to heaven instead of hell (class), after one dies (filter!). Herein, death serves as the filter, le | Biased Data in Machine Learning
This is akin to the after-life dilemma: what ratio of good and bad deeds (data) is sufficient to get to heaven instead of hell (class), after one dies (filter!). Herein, death serves as the filter, leading to missing values towards a supervised learning scheme.
I want to disambiguate bet... | Biased Data in Machine Learning
This is akin to the after-life dilemma: what ratio of good and bad deeds (data) is sufficient to get to heaven instead of hell (class), after one dies (filter!). Herein, death serves as the filter, le |
14,745 | Derivative of a Gaussian Process | The short answer: Yes, if your Gaussian Process (GP) is differentiable, its derivative is again a GP. It can be handled like any other GP and you can calculate predictive distributions.
But since a GP $G$ and its derivative $G'$ are closely related you can infer properties of either one from the other.
Existence of $... | Derivative of a Gaussian Process | The short answer: Yes, if your Gaussian Process (GP) is differentiable, its derivative is again a GP. It can be handled like any other GP and you can calculate predictive distributions.
But since a G | Derivative of a Gaussian Process
The short answer: Yes, if your Gaussian Process (GP) is differentiable, its derivative is again a GP. It can be handled like any other GP and you can calculate predictive distributions.
But since a GP $G$ and its derivative $G'$ are closely related you can infer properties of either on... | Derivative of a Gaussian Process
The short answer: Yes, if your Gaussian Process (GP) is differentiable, its derivative is again a GP. It can be handled like any other GP and you can calculate predictive distributions.
But since a G |
14,746 | Derivative of a Gaussian Process | It is. See Rasmussen and Williams section 9.4. Also, some authors argue strongly against the square exponential kenrnel - it is too smooth. | Derivative of a Gaussian Process | It is. See Rasmussen and Williams section 9.4. Also, some authors argue strongly against the square exponential kenrnel - it is too smooth. | Derivative of a Gaussian Process
It is. See Rasmussen and Williams section 9.4. Also, some authors argue strongly against the square exponential kenrnel - it is too smooth. | Derivative of a Gaussian Process
It is. See Rasmussen and Williams section 9.4. Also, some authors argue strongly against the square exponential kenrnel - it is too smooth. |
14,747 | Clustering of very skewed, count data: any suggestions to go about (transform etc)? | It is not wise to transform the variables individually because they belong together (as you noticed) and to do k-means because the data are counts (you might, but k-means is better to do on continuous attributes such as length for example).
In your place, I would compute chi-square distance (perfect for counts) between... | Clustering of very skewed, count data: any suggestions to go about (transform etc)? | It is not wise to transform the variables individually because they belong together (as you noticed) and to do k-means because the data are counts (you might, but k-means is better to do on continuous | Clustering of very skewed, count data: any suggestions to go about (transform etc)?
It is not wise to transform the variables individually because they belong together (as you noticed) and to do k-means because the data are counts (you might, but k-means is better to do on continuous attributes such as length for examp... | Clustering of very skewed, count data: any suggestions to go about (transform etc)?
It is not wise to transform the variables individually because they belong together (as you noticed) and to do k-means because the data are counts (you might, but k-means is better to do on continuous |
14,748 | Clustering of very skewed, count data: any suggestions to go about (transform etc)? | @ttnphns has provided a good answer.
Doing clustering well is often about thinking very hard about your data, so let's do some of that. To my mind, the most fundamental aspect of your data is that they are compositional.
On the other hand, your primary concern seems to be that you have a lot of 0s for green produc... | Clustering of very skewed, count data: any suggestions to go about (transform etc)? | @ttnphns has provided a good answer.
Doing clustering well is often about thinking very hard about your data, so let's do some of that. To my mind, the most fundamental aspect of your data is that | Clustering of very skewed, count data: any suggestions to go about (transform etc)?
@ttnphns has provided a good answer.
Doing clustering well is often about thinking very hard about your data, so let's do some of that. To my mind, the most fundamental aspect of your data is that they are compositional.
On the oth... | Clustering of very skewed, count data: any suggestions to go about (transform etc)?
@ttnphns has provided a good answer.
Doing clustering well is often about thinking very hard about your data, so let's do some of that. To my mind, the most fundamental aspect of your data is that |
14,749 | What are the regularity conditions for Likelihood Ratio test | The required regularity conditions are listed in most intermediate textbooks and are not different than those of the mle. The following ones concern the one parameter case yet their extension to the multiparameter one is straightforward.
Condition 1: The pdfs are distinct, i.e. $\theta \neq \theta ^{\prime} \Rightarrow... | What are the regularity conditions for Likelihood Ratio test | The required regularity conditions are listed in most intermediate textbooks and are not different than those of the mle. The following ones concern the one parameter case yet their extension to the m | What are the regularity conditions for Likelihood Ratio test
The required regularity conditions are listed in most intermediate textbooks and are not different than those of the mle. The following ones concern the one parameter case yet their extension to the multiparameter one is straightforward.
Condition 1: The pdfs... | What are the regularity conditions for Likelihood Ratio test
The required regularity conditions are listed in most intermediate textbooks and are not different than those of the mle. The following ones concern the one parameter case yet their extension to the m |
14,750 | Extract data points from moving average? | +1 to fabee's answer, which is complete. Just a note to translate it into R, based on the packages that I've found to do the operations at hand. In my case, I had data that is NOAA temperature forecasts on a three-month basis: Jan-Feb-Mar, Feb-Mar-Apr, Mar-Apr-May, etc, and I wanted to break it out into (approximate) m... | Extract data points from moving average? | +1 to fabee's answer, which is complete. Just a note to translate it into R, based on the packages that I've found to do the operations at hand. In my case, I had data that is NOAA temperature forecas | Extract data points from moving average?
+1 to fabee's answer, which is complete. Just a note to translate it into R, based on the packages that I've found to do the operations at hand. In my case, I had data that is NOAA temperature forecasts on a three-month basis: Jan-Feb-Mar, Feb-Mar-Apr, Mar-Apr-May, etc, and I wa... | Extract data points from moving average?
+1 to fabee's answer, which is complete. Just a note to translate it into R, based on the packages that I've found to do the operations at hand. In my case, I had data that is NOAA temperature forecas |
14,751 | Extract data points from moving average? | I try to put what whuber said into an answer. Let's say you have a large vector $\mathbf x$ with $n=2000$ entries. If you compute a moving average with a window of length $\ell=30$, you can write this as a vector matrix multiplication $\mathbf y = A\mathbf x$ of the vector $\mathbf x$ with the matrix
$$A=\frac{1}{30}\... | Extract data points from moving average? | I try to put what whuber said into an answer. Let's say you have a large vector $\mathbf x$ with $n=2000$ entries. If you compute a moving average with a window of length $\ell=30$, you can write this | Extract data points from moving average?
I try to put what whuber said into an answer. Let's say you have a large vector $\mathbf x$ with $n=2000$ entries. If you compute a moving average with a window of length $\ell=30$, you can write this as a vector matrix multiplication $\mathbf y = A\mathbf x$ of the vector $\mat... | Extract data points from moving average?
I try to put what whuber said into an answer. Let's say you have a large vector $\mathbf x$ with $n=2000$ entries. If you compute a moving average with a window of length $\ell=30$, you can write this |
14,752 | Extract data points from moving average? | This is very related with this question cumsum with shift of n I asked in SO.
I also answered in SO the same question as this one but it has been closed so I include here the answer again because I think is more focus in the software implementation than from the mathematical understanding (even though I think they are ... | Extract data points from moving average? | This is very related with this question cumsum with shift of n I asked in SO.
I also answered in SO the same question as this one but it has been closed so I include here the answer again because I th | Extract data points from moving average?
This is very related with this question cumsum with shift of n I asked in SO.
I also answered in SO the same question as this one but it has been closed so I include here the answer again because I think is more focus in the software implementation than from the mathematical und... | Extract data points from moving average?
This is very related with this question cumsum with shift of n I asked in SO.
I also answered in SO the same question as this one but it has been closed so I include here the answer again because I th |
14,753 | Extract data points from moving average? | Gonzalo,
I'm using your cumsum_shift function in my large df (400,000 points) but I have problems when I change the win_size. Figure below is for win_size=12,000 and I can see some spikes at the end of each win_size. For my current problem I need to use win_size> 40,000. Do you have any idea of restriction of your func... | Extract data points from moving average? | Gonzalo,
I'm using your cumsum_shift function in my large df (400,000 points) but I have problems when I change the win_size. Figure below is for win_size=12,000 and I can see some spikes at the end o | Extract data points from moving average?
Gonzalo,
I'm using your cumsum_shift function in my large df (400,000 points) but I have problems when I change the win_size. Figure below is for win_size=12,000 and I can see some spikes at the end of each win_size. For my current problem I need to use win_size> 40,000. Do you ... | Extract data points from moving average?
Gonzalo,
I'm using your cumsum_shift function in my large df (400,000 points) but I have problems when I change the win_size. Figure below is for win_size=12,000 and I can see some spikes at the end o |
14,754 | Extract data points from moving average? | fabee's answer was complete. I am just adding a generic function that can be used in Python that I've created and tested for my projects (with a sample code)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def reconstruct_orig(sm_x:np.ndarray, win_size:int=7):
"""reconstructing from origina... | Extract data points from moving average? | fabee's answer was complete. I am just adding a generic function that can be used in Python that I've created and tested for my projects (with a sample code)
import numpy as np
import pandas as pd
imp | Extract data points from moving average?
fabee's answer was complete. I am just adding a generic function that can be used in Python that I've created and tested for my projects (with a sample code)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def reconstruct_orig(sm_x:np.ndarray, win_size:i... | Extract data points from moving average?
fabee's answer was complete. I am just adding a generic function that can be used in Python that I've created and tested for my projects (with a sample code)
import numpy as np
import pandas as pd
imp |
14,755 | Why is statistics useful when many things that matter are one shot things? | First I think that you may be confusing "statistics" meaning a collection of numbers or other facts describing a group or situation, and "statistics" meaning the science of using data and information to understand the world in the face of variation (others may be able to improve on my definitions). Statisticians use b... | Why is statistics useful when many things that matter are one shot things? | First I think that you may be confusing "statistics" meaning a collection of numbers or other facts describing a group or situation, and "statistics" meaning the science of using data and information | Why is statistics useful when many things that matter are one shot things?
First I think that you may be confusing "statistics" meaning a collection of numbers or other facts describing a group or situation, and "statistics" meaning the science of using data and information to understand the world in the face of variat... | Why is statistics useful when many things that matter are one shot things?
First I think that you may be confusing "statistics" meaning a collection of numbers or other facts describing a group or situation, and "statistics" meaning the science of using data and information |
14,756 | Why is statistics useful when many things that matter are one shot things? | Just because you don't use statistics in your daily life does not mean that the field does not directly affect you. When you are at the doctor and they recommend one treatment over the other, you can bet that behind that recommendation was many clinical trials that used statistics to interpret the results of their expe... | Why is statistics useful when many things that matter are one shot things? | Just because you don't use statistics in your daily life does not mean that the field does not directly affect you. When you are at the doctor and they recommend one treatment over the other, you can | Why is statistics useful when many things that matter are one shot things?
Just because you don't use statistics in your daily life does not mean that the field does not directly affect you. When you are at the doctor and they recommend one treatment over the other, you can bet that behind that recommendation was many ... | Why is statistics useful when many things that matter are one shot things?
Just because you don't use statistics in your daily life does not mean that the field does not directly affect you. When you are at the doctor and they recommend one treatment over the other, you can |
14,757 | Why is statistics useful when many things that matter are one shot things? | The world is stochastic not deterministic. If it were deterministic the physicists would be ruling the world and statisticians would be out of a job. But the reality is that statisticians are in high demand in almost every discipline. That is not to say that there isn't a place for physics and other sciences but sta... | Why is statistics useful when many things that matter are one shot things? | The world is stochastic not deterministic. If it were deterministic the physicists would be ruling the world and statisticians would be out of a job. But the reality is that statisticians are in hig | Why is statistics useful when many things that matter are one shot things?
The world is stochastic not deterministic. If it were deterministic the physicists would be ruling the world and statisticians would be out of a job. But the reality is that statisticians are in high demand in almost every discipline. That is... | Why is statistics useful when many things that matter are one shot things?
The world is stochastic not deterministic. If it were deterministic the physicists would be ruling the world and statisticians would be out of a job. But the reality is that statisticians are in hig |
14,758 | Why is statistics useful when many things that matter are one shot things? | I myself have the same doubts about the usefulness of probability, and statistics, when it comes to taking decision about a single event. In my opinion, knowing the probability, real or estimated, is extremely important when the objective is estimating outcomes of samples, be they a single event repeated a number of ti... | Why is statistics useful when many things that matter are one shot things? | I myself have the same doubts about the usefulness of probability, and statistics, when it comes to taking decision about a single event. In my opinion, knowing the probability, real or estimated, is | Why is statistics useful when many things that matter are one shot things?
I myself have the same doubts about the usefulness of probability, and statistics, when it comes to taking decision about a single event. In my opinion, knowing the probability, real or estimated, is extremely important when the objective is est... | Why is statistics useful when many things that matter are one shot things?
I myself have the same doubts about the usefulness of probability, and statistics, when it comes to taking decision about a single event. In my opinion, knowing the probability, real or estimated, is |
14,759 | Why is statistics useful when many things that matter are one shot things? | The long and the short of it is that probability is the unique generalization of ordinary true/false logic to degrees of belief between 0 and 1. This is the so-called logical Bayesian interpretation of probability, originated by R.T. Cox and later championed by E.T. Jaynes.
Furthermore under weak assumptions it can be ... | Why is statistics useful when many things that matter are one shot things? | The long and the short of it is that probability is the unique generalization of ordinary true/false logic to degrees of belief between 0 and 1. This is the so-called logical Bayesian interpretation o | Why is statistics useful when many things that matter are one shot things?
The long and the short of it is that probability is the unique generalization of ordinary true/false logic to degrees of belief between 0 and 1. This is the so-called logical Bayesian interpretation of probability, originated by R.T. Cox and lat... | Why is statistics useful when many things that matter are one shot things?
The long and the short of it is that probability is the unique generalization of ordinary true/false logic to degrees of belief between 0 and 1. This is the so-called logical Bayesian interpretation o |
14,760 | Why is statistics useful when many things that matter are one shot things? | Want to improve this post? Add citations from reputable sources by editing the post. Posts with unsourced content may be edited or deleted.
I skeptical of statistics for the following reasons.
I am convinced anybody without a graduate degree in sta... | Why is statistics useful when many things that matter are one shot things? | Want to improve this post? Add citations from reputable sources by editing the post. Posts with unsourced content may be edited or deleted.
| Why is statistics useful when many things that matter are one shot things?
Want to improve this post? Add citations from reputable sources by editing the post. Posts with unsourced content may be edited or deleted.
I skeptical of statistics for the f... | Why is statistics useful when many things that matter are one shot things?
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|
14,761 | how to avoid overfitting in XGBoost model | XGBoost (and other gradient boosting machine routines too) has a number of parameters that can be tuned to avoid over-fitting. I will mention some of the most obvious ones. For example we can change:
the ratio of features used (i.e. columns used); colsample_bytree. Lower ratios avoid over-fitting.
the ratio of the tra... | how to avoid overfitting in XGBoost model | XGBoost (and other gradient boosting machine routines too) has a number of parameters that can be tuned to avoid over-fitting. I will mention some of the most obvious ones. For example we can change:
| how to avoid overfitting in XGBoost model
XGBoost (and other gradient boosting machine routines too) has a number of parameters that can be tuned to avoid over-fitting. I will mention some of the most obvious ones. For example we can change:
the ratio of features used (i.e. columns used); colsample_bytree. Lower ratio... | how to avoid overfitting in XGBoost model
XGBoost (and other gradient boosting machine routines too) has a number of parameters that can be tuned to avoid over-fitting. I will mention some of the most obvious ones. For example we can change:
|
14,762 | How should I intuitively understand the KL divergence loss in variational autoencoders? [duplicate] | The KL divergence tells us how well the probability distribution Q approximates the probability distribution P by calculating the cross-entropy minus the entropy. Intuitively, you can think of that as the statistical measure of how one distribution differs from another.
In VAE, let $X$ be the data we want to model, $z... | How should I intuitively understand the KL divergence loss in variational autoencoders? [duplicate] | The KL divergence tells us how well the probability distribution Q approximates the probability distribution P by calculating the cross-entropy minus the entropy. Intuitively, you can think of that as | How should I intuitively understand the KL divergence loss in variational autoencoders? [duplicate]
The KL divergence tells us how well the probability distribution Q approximates the probability distribution P by calculating the cross-entropy minus the entropy. Intuitively, you can think of that as the statistical mea... | How should I intuitively understand the KL divergence loss in variational autoencoders? [duplicate]
The KL divergence tells us how well the probability distribution Q approximates the probability distribution P by calculating the cross-entropy minus the entropy. Intuitively, you can think of that as |
14,763 | The pdf of $\frac{X_1-\bar{X}}{S}$ | What is so intriguing about this result is how much it looks like the distribution of a correlation coefficient. There's a reason.
Suppose $(X,Y)$ is bivariate normal with zero correlation and common variance $\sigma^2$ for both variables. Draw an iid sample $(x_1,y_1), \ldots, (x_n,y_n)$. It is well known, and rea... | The pdf of $\frac{X_1-\bar{X}}{S}$ | What is so intriguing about this result is how much it looks like the distribution of a correlation coefficient. There's a reason.
Suppose $(X,Y)$ is bivariate normal with zero correlation and commo | The pdf of $\frac{X_1-\bar{X}}{S}$
What is so intriguing about this result is how much it looks like the distribution of a correlation coefficient. There's a reason.
Suppose $(X,Y)$ is bivariate normal with zero correlation and common variance $\sigma^2$ for both variables. Draw an iid sample $(x_1,y_1), \ldots, (x_... | The pdf of $\frac{X_1-\bar{X}}{S}$
What is so intriguing about this result is how much it looks like the distribution of a correlation coefficient. There's a reason.
Suppose $(X,Y)$ is bivariate normal with zero correlation and commo |
14,764 | The pdf of $\frac{X_1-\bar{X}}{S}$ | I'd like to suggest this way to get the pdf of Z by directly calculating the MVUE of $P(X\leq c)$ using Bayes' theorem although it's handful and complex.
Since $E[I_{(-\infty,c)}(X_1)]=P(X_1\leq c)$ and $Z_1=\bar X$, $Z_2=S^2$ are joint complete sufficient statistic, MVUE of $P(X\leq c)$ would be like this:
$$\psi(z_1... | The pdf of $\frac{X_1-\bar{X}}{S}$ | I'd like to suggest this way to get the pdf of Z by directly calculating the MVUE of $P(X\leq c)$ using Bayes' theorem although it's handful and complex.
Since $E[I_{(-\infty,c)}(X_1)]=P(X_1\leq c)$ a | The pdf of $\frac{X_1-\bar{X}}{S}$
I'd like to suggest this way to get the pdf of Z by directly calculating the MVUE of $P(X\leq c)$ using Bayes' theorem although it's handful and complex.
Since $E[I_{(-\infty,c)}(X_1)]=P(X_1\leq c)$ and $Z_1=\bar X$, $Z_2=S^2$ are joint complete sufficient statistic, MVUE of $P(X\leq... | The pdf of $\frac{X_1-\bar{X}}{S}$
I'd like to suggest this way to get the pdf of Z by directly calculating the MVUE of $P(X\leq c)$ using Bayes' theorem although it's handful and complex.
Since $E[I_{(-\infty,c)}(X_1)]=P(X_1\leq c)$ a |
14,765 | What is an intuitive explanation of Echo State Networks? | An Echo State Network is an instance of the more general concept of Reservoir Computing. The basic idea behind the ESN is to get the benefits of a RNN (process a sequence of inputs that are dependent on each other, i.e. time dependencies like a signal) but without the problems of training a traditional RNN like the van... | What is an intuitive explanation of Echo State Networks? | An Echo State Network is an instance of the more general concept of Reservoir Computing. The basic idea behind the ESN is to get the benefits of a RNN (process a sequence of inputs that are dependent | What is an intuitive explanation of Echo State Networks?
An Echo State Network is an instance of the more general concept of Reservoir Computing. The basic idea behind the ESN is to get the benefits of a RNN (process a sequence of inputs that are dependent on each other, i.e. time dependencies like a signal) but withou... | What is an intuitive explanation of Echo State Networks?
An Echo State Network is an instance of the more general concept of Reservoir Computing. The basic idea behind the ESN is to get the benefits of a RNN (process a sequence of inputs that are dependent |
14,766 | What is an intuitive explanation of Echo State Networks? | Learning in an ESN isn’t primary forced to adapting weights, more respectively the output layer learns which output to produce for the current state the network has. The internal state is based on network dynamics and is called dynamic reservoir state. To understand how the reservoir states shape out, we need to look a... | What is an intuitive explanation of Echo State Networks? | Learning in an ESN isn’t primary forced to adapting weights, more respectively the output layer learns which output to produce for the current state the network has. The internal state is based on net | What is an intuitive explanation of Echo State Networks?
Learning in an ESN isn’t primary forced to adapting weights, more respectively the output layer learns which output to produce for the current state the network has. The internal state is based on network dynamics and is called dynamic reservoir state. To underst... | What is an intuitive explanation of Echo State Networks?
Learning in an ESN isn’t primary forced to adapting weights, more respectively the output layer learns which output to produce for the current state the network has. The internal state is based on net |
14,767 | Does a seasonal time series imply a stationary or a non stationary time series | Seasonality does not make your series non-stationary. The stationarity applies to the errors of your data generating process, e.g. $y_t=sin(t)+\varepsilon_t$, where $\varepsilon_t\sim\mathcal{N}(0,\sigma^2)$ and $Cov[\varepsilon_s,\varepsilon_t]=\sigma^21_{s=t}$ is a stationary process, despite having a periodic wave i... | Does a seasonal time series imply a stationary or a non stationary time series | Seasonality does not make your series non-stationary. The stationarity applies to the errors of your data generating process, e.g. $y_t=sin(t)+\varepsilon_t$, where $\varepsilon_t\sim\mathcal{N}(0,\si | Does a seasonal time series imply a stationary or a non stationary time series
Seasonality does not make your series non-stationary. The stationarity applies to the errors of your data generating process, e.g. $y_t=sin(t)+\varepsilon_t$, where $\varepsilon_t\sim\mathcal{N}(0,\sigma^2)$ and $Cov[\varepsilon_s,\varepsilo... | Does a seasonal time series imply a stationary or a non stationary time series
Seasonality does not make your series non-stationary. The stationarity applies to the errors of your data generating process, e.g. $y_t=sin(t)+\varepsilon_t$, where $\varepsilon_t\sim\mathcal{N}(0,\si |
14,768 | Does a seasonal time series imply a stationary or a non stationary time series | IMHO, persistent seasonality, by definition, is a type of non-stationarity: the mean of a seasonal process varies with the season, E[z(t*s+j)] = f(j), where s is the number of seasons, j is a particular season (j=1,...,s), and t is specific period (typically a year). Thus, E[y(t)] = E[sin(t)+u(t)] = sin(t) is not a sta... | Does a seasonal time series imply a stationary or a non stationary time series | IMHO, persistent seasonality, by definition, is a type of non-stationarity: the mean of a seasonal process varies with the season, E[z(t*s+j)] = f(j), where s is the number of seasons, j is a particul | Does a seasonal time series imply a stationary or a non stationary time series
IMHO, persistent seasonality, by definition, is a type of non-stationarity: the mean of a seasonal process varies with the season, E[z(t*s+j)] = f(j), where s is the number of seasons, j is a particular season (j=1,...,s), and t is specific ... | Does a seasonal time series imply a stationary or a non stationary time series
IMHO, persistent seasonality, by definition, is a type of non-stationarity: the mean of a seasonal process varies with the season, E[z(t*s+j)] = f(j), where s is the number of seasons, j is a particul |
14,769 | Does a seasonal time series imply a stationary or a non stationary time series | A seasonal pattern that remains stable over time does not make the series non-stationary. A non-stable seasonal pattern, for example a seasonal random walk, will make the data non-stationary.
Edit (after new answer and comments)
A stable seasonal pattern is not stationary in the sense that the mean of the series will v... | Does a seasonal time series imply a stationary or a non stationary time series | A seasonal pattern that remains stable over time does not make the series non-stationary. A non-stable seasonal pattern, for example a seasonal random walk, will make the data non-stationary.
Edit (af | Does a seasonal time series imply a stationary or a non stationary time series
A seasonal pattern that remains stable over time does not make the series non-stationary. A non-stable seasonal pattern, for example a seasonal random walk, will make the data non-stationary.
Edit (after new answer and comments)
A stable sea... | Does a seasonal time series imply a stationary or a non stationary time series
A seasonal pattern that remains stable over time does not make the series non-stationary. A non-stable seasonal pattern, for example a seasonal random walk, will make the data non-stationary.
Edit (af |
14,770 | Does a seasonal time series imply a stationary or a non stationary time series | I don`t agree that seasonality is a type of non-stationarity because the concept of stationarity in natural systems already incorporates the idea of fluctuation within an unchanging envelope of variability (Milly et al., 2008).
Speaking about hydrological time-series, even though they are stochastic (random process) an... | Does a seasonal time series imply a stationary or a non stationary time series | I don`t agree that seasonality is a type of non-stationarity because the concept of stationarity in natural systems already incorporates the idea of fluctuation within an unchanging envelope of variab | Does a seasonal time series imply a stationary or a non stationary time series
I don`t agree that seasonality is a type of non-stationarity because the concept of stationarity in natural systems already incorporates the idea of fluctuation within an unchanging envelope of variability (Milly et al., 2008).
Speaking abou... | Does a seasonal time series imply a stationary or a non stationary time series
I don`t agree that seasonality is a type of non-stationarity because the concept of stationarity in natural systems already incorporates the idea of fluctuation within an unchanging envelope of variab |
14,771 | Effective Sample Size for posterior inference from MCMC sampling | The question you are asking is different from "convergence diagnostics". Lets say you have run all convergence diagnostics(choose your favorite(s)), and now are ready to start sampling from the posterior.
There are two options in terms of effective sample size(ESS), you can choose a univariate ESS or a multivariate ES... | Effective Sample Size for posterior inference from MCMC sampling | The question you are asking is different from "convergence diagnostics". Lets say you have run all convergence diagnostics(choose your favorite(s)), and now are ready to start sampling from the poster | Effective Sample Size for posterior inference from MCMC sampling
The question you are asking is different from "convergence diagnostics". Lets say you have run all convergence diagnostics(choose your favorite(s)), and now are ready to start sampling from the posterior.
There are two options in terms of effective sampl... | Effective Sample Size for posterior inference from MCMC sampling
The question you are asking is different from "convergence diagnostics". Lets say you have run all convergence diagnostics(choose your favorite(s)), and now are ready to start sampling from the poster |
14,772 | Effective Sample Size for posterior inference from MCMC sampling | The convergence depends on several things: the number of parameters, the model itself, the sampling algorithm, the data ...
I would suggests to avoid any general rule and to employ a couple of convergence diagnostics tools to detect appropriate burn-in and thinning number of iterations in each specific example. See als... | Effective Sample Size for posterior inference from MCMC sampling | The convergence depends on several things: the number of parameters, the model itself, the sampling algorithm, the data ...
I would suggests to avoid any general rule and to employ a couple of converg | Effective Sample Size for posterior inference from MCMC sampling
The convergence depends on several things: the number of parameters, the model itself, the sampling algorithm, the data ...
I would suggests to avoid any general rule and to employ a couple of convergence diagnostics tools to detect appropriate burn-in an... | Effective Sample Size for posterior inference from MCMC sampling
The convergence depends on several things: the number of parameters, the model itself, the sampling algorithm, the data ...
I would suggests to avoid any general rule and to employ a couple of converg |
14,773 | Two-dimensional Kolmogorov–Smirnov | Python implementation
I have written a python implementation using numpy. You can find the code here, you may find more infomation in the docstring in the code.
And here's another one (not by me). This Notebook provide a Python implementation for 2D K-S test with 2 samples. The .py file can be downloaded here. The code... | Two-dimensional Kolmogorov–Smirnov | Python implementation
I have written a python implementation using numpy. You can find the code here, you may find more infomation in the docstring in the code.
And here's another one (not by me). Thi | Two-dimensional Kolmogorov–Smirnov
Python implementation
I have written a python implementation using numpy. You can find the code here, you may find more infomation in the docstring in the code.
And here's another one (not by me). This Notebook provide a Python implementation for 2D K-S test with 2 samples. The .py fi... | Two-dimensional Kolmogorov–Smirnov
Python implementation
I have written a python implementation using numpy. You can find the code here, you may find more infomation in the docstring in the code.
And here's another one (not by me). Thi |
14,774 | Two-dimensional Kolmogorov–Smirnov | A two-dimensional extension of the Kolmogorov-Smirnov test has been described by Justel, Pena and Zamar in a "A multivariate Komogorov-Smirnov test of goodness of fit". @Procrastinator's comments suggests there may be other such proposals.
However, I haven't seen a package with a straightforward implementation.
Depe... | Two-dimensional Kolmogorov–Smirnov | A two-dimensional extension of the Kolmogorov-Smirnov test has been described by Justel, Pena and Zamar in a "A multivariate Komogorov-Smirnov test of goodness of fit". @Procrastinator's comments sug | Two-dimensional Kolmogorov–Smirnov
A two-dimensional extension of the Kolmogorov-Smirnov test has been described by Justel, Pena and Zamar in a "A multivariate Komogorov-Smirnov test of goodness of fit". @Procrastinator's comments suggests there may be other such proposals.
However, I haven't seen a package with a s... | Two-dimensional Kolmogorov–Smirnov
A two-dimensional extension of the Kolmogorov-Smirnov test has been described by Justel, Pena and Zamar in a "A multivariate Komogorov-Smirnov test of goodness of fit". @Procrastinator's comments sug |
14,775 | Two-dimensional Kolmogorov–Smirnov | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
you may find this Matlab code to be useful.
http://ww... | Two-dimensional Kolmogorov–Smirnov | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| Two-dimensional Kolmogorov–Smirnov
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
you may find this M... | Two-dimensional Kolmogorov–Smirnov
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
14,776 | What are the sharpest known tail bounds for $\chi_k^2$ distributed variables? | The Sharpest bound I know is that of Massart and Laurent Lemma 1 p1325.
A corollary of their bound is:
$$P(X-k\geq 2\sqrt{kx}+2x)\leq \exp (-x) $$
$$P(k-X\geq 2\sqrt{kx})\leq \exp (-x) $$ | What are the sharpest known tail bounds for $\chi_k^2$ distributed variables? | The Sharpest bound I know is that of Massart and Laurent Lemma 1 p1325.
A corollary of their bound is:
$$P(X-k\geq 2\sqrt{kx}+2x)\leq \exp (-x) $$
$$P(k-X\geq 2\sqrt{kx})\leq \exp (-x) $$ | What are the sharpest known tail bounds for $\chi_k^2$ distributed variables?
The Sharpest bound I know is that of Massart and Laurent Lemma 1 p1325.
A corollary of their bound is:
$$P(X-k\geq 2\sqrt{kx}+2x)\leq \exp (-x) $$
$$P(k-X\geq 2\sqrt{kx})\leq \exp (-x) $$ | What are the sharpest known tail bounds for $\chi_k^2$ distributed variables?
The Sharpest bound I know is that of Massart and Laurent Lemma 1 p1325.
A corollary of their bound is:
$$P(X-k\geq 2\sqrt{kx}+2x)\leq \exp (-x) $$
$$P(k-X\geq 2\sqrt{kx})\leq \exp (-x) $$ |
14,777 | Mathematical/Algorithmic definition for overfitting | Yes there is a (slightly more) rigorous definition:
Given a model with a set of parameters, the model can be said to be overfitting the data if after a certain number of training steps, the training error continues to decrease while the out of sample (test) error starts increasing.
In this example out of sample (te... | Mathematical/Algorithmic definition for overfitting | Yes there is a (slightly more) rigorous definition:
Given a model with a set of parameters, the model can be said to be overfitting the data if after a certain number of training steps, the training | Mathematical/Algorithmic definition for overfitting
Yes there is a (slightly more) rigorous definition:
Given a model with a set of parameters, the model can be said to be overfitting the data if after a certain number of training steps, the training error continues to decrease while the out of sample (test) error sta... | Mathematical/Algorithmic definition for overfitting
Yes there is a (slightly more) rigorous definition:
Given a model with a set of parameters, the model can be said to be overfitting the data if after a certain number of training steps, the training |
14,778 | What is inverse CDF Normal Distribution Formula | There's no closed form expression for the inverse cdf of a normal (a.k.a. the quantile function of a normal). It looks like this:
There are various ways to express the function (e.g. as an infinite series or as a continued fraction), and numerous approximations (which is how computers are able to "calculate" it).
Reas... | What is inverse CDF Normal Distribution Formula | There's no closed form expression for the inverse cdf of a normal (a.k.a. the quantile function of a normal). It looks like this:
There are various ways to express the function (e.g. as an infinite s | What is inverse CDF Normal Distribution Formula
There's no closed form expression for the inverse cdf of a normal (a.k.a. the quantile function of a normal). It looks like this:
There are various ways to express the function (e.g. as an infinite series or as a continued fraction), and numerous approximations (which is... | What is inverse CDF Normal Distribution Formula
There's no closed form expression for the inverse cdf of a normal (a.k.a. the quantile function of a normal). It looks like this:
There are various ways to express the function (e.g. as an infinite s |
14,779 | Can one-sided confidence intervals have 95% coverage | Yes we can construct one sided confidence intervals with 95% coverage.
The two sided confidence interval corresponds to the critical values in a two-tailed hypothesis test, the same applies to one sided confidence intervals and one-tailed hypothesis tests.
For example, if you have data with sample statistics $\bar{x}=... | Can one-sided confidence intervals have 95% coverage | Yes we can construct one sided confidence intervals with 95% coverage.
The two sided confidence interval corresponds to the critical values in a two-tailed hypothesis test, the same applies to one si | Can one-sided confidence intervals have 95% coverage
Yes we can construct one sided confidence intervals with 95% coverage.
The two sided confidence interval corresponds to the critical values in a two-tailed hypothesis test, the same applies to one sided confidence intervals and one-tailed hypothesis tests.
For examp... | Can one-sided confidence intervals have 95% coverage
Yes we can construct one sided confidence intervals with 95% coverage.
The two sided confidence interval corresponds to the critical values in a two-tailed hypothesis test, the same applies to one si |
14,780 | What is the VC dimension of a decision tree? | I'm not sure this is a question with a simple answer, nor do I believe it is a question that even needs to be asked about decision trees.
Consult Aslan et al., Calculating the VC-Dimension of Trees (2009). They address this problem by doing an exhaustive search, in small trees, and then providing an approximate, recurs... | What is the VC dimension of a decision tree? | I'm not sure this is a question with a simple answer, nor do I believe it is a question that even needs to be asked about decision trees.
Consult Aslan et al., Calculating the VC-Dimension of Trees (2 | What is the VC dimension of a decision tree?
I'm not sure this is a question with a simple answer, nor do I believe it is a question that even needs to be asked about decision trees.
Consult Aslan et al., Calculating the VC-Dimension of Trees (2009). They address this problem by doing an exhaustive search, in small tre... | What is the VC dimension of a decision tree?
I'm not sure this is a question with a simple answer, nor do I believe it is a question that even needs to be asked about decision trees.
Consult Aslan et al., Calculating the VC-Dimension of Trees (2 |
14,781 | What is the VC dimension of a decision tree? | I know this post is kind of old and already has an accepted answered, but as it is the first to link appear on Google when asking about the VC dimension of decision trees, I will allow myself to give some new information as a follow up.
In a recent paper, Decision trees as partitioning machines to characterize their ge... | What is the VC dimension of a decision tree? | I know this post is kind of old and already has an accepted answered, but as it is the first to link appear on Google when asking about the VC dimension of decision trees, I will allow myself to give | What is the VC dimension of a decision tree?
I know this post is kind of old and already has an accepted answered, but as it is the first to link appear on Google when asking about the VC dimension of decision trees, I will allow myself to give some new information as a follow up.
In a recent paper, Decision trees as p... | What is the VC dimension of a decision tree?
I know this post is kind of old and already has an accepted answered, but as it is the first to link appear on Google when asking about the VC dimension of decision trees, I will allow myself to give |
14,782 | What are Bayesian p-values? | If I understand it correctly, then a Bayesian p-value is the comparison of a some metric calculated from your observed data with the same metric calculated from your simulated data (being generated with parameters drawn from the posterior distribution).
In Gelmans words: "From a Bayesian context, a posterior p-value is... | What are Bayesian p-values? | If I understand it correctly, then a Bayesian p-value is the comparison of a some metric calculated from your observed data with the same metric calculated from your simulated data (being generated wi | What are Bayesian p-values?
If I understand it correctly, then a Bayesian p-value is the comparison of a some metric calculated from your observed data with the same metric calculated from your simulated data (being generated with parameters drawn from the posterior distribution).
In Gelmans words: "From a Bayesian con... | What are Bayesian p-values?
If I understand it correctly, then a Bayesian p-value is the comparison of a some metric calculated from your observed data with the same metric calculated from your simulated data (being generated wi |
14,783 | What are Bayesian p-values? | Bayesian p-values are normally used when one would like to check how a model fits the data. That is, given a model $M$ we wish to examine how well it fits the observed data $x_{obs}$ based on a statistic $T$, which measures the goodness of fit of data and model. For this, suppose we have a model $M$ with probability de... | What are Bayesian p-values? | Bayesian p-values are normally used when one would like to check how a model fits the data. That is, given a model $M$ we wish to examine how well it fits the observed data $x_{obs}$ based on a statis | What are Bayesian p-values?
Bayesian p-values are normally used when one would like to check how a model fits the data. That is, given a model $M$ we wish to examine how well it fits the observed data $x_{obs}$ based on a statistic $T$, which measures the goodness of fit of data and model. For this, suppose we have a m... | What are Bayesian p-values?
Bayesian p-values are normally used when one would like to check how a model fits the data. That is, given a model $M$ we wish to examine how well it fits the observed data $x_{obs}$ based on a statis |
14,784 | What is the long run variance? | It is a measure of the standard error of the sample mean when there is serial dependence.
If $Y_t$ is covariance stationary with $E(Y_t)=\mu$ and $Cov(Y_t,Y_{t-j})=\gamma_j$ (in an iid setting, this quantity would be zero!) such that $\sum_{j=0}^\infty|\gamma_j|<\infty$. Then
$$\lim_{T\to\infty}\{Var[\sqrt{T}(\bar{Y}_T... | What is the long run variance? | It is a measure of the standard error of the sample mean when there is serial dependence.
If $Y_t$ is covariance stationary with $E(Y_t)=\mu$ and $Cov(Y_t,Y_{t-j})=\gamma_j$ (in an iid setting, this q | What is the long run variance?
It is a measure of the standard error of the sample mean when there is serial dependence.
If $Y_t$ is covariance stationary with $E(Y_t)=\mu$ and $Cov(Y_t,Y_{t-j})=\gamma_j$ (in an iid setting, this quantity would be zero!) such that $\sum_{j=0}^\infty|\gamma_j|<\infty$. Then
$$\lim_{T\to... | What is the long run variance?
It is a measure of the standard error of the sample mean when there is serial dependence.
If $Y_t$ is covariance stationary with $E(Y_t)=\mu$ and $Cov(Y_t,Y_{t-j})=\gamma_j$ (in an iid setting, this q |
14,785 | For what (symmetric) distributions is sample mean a more efficient estimator than sample median? | Let's assume we restrict consideration to symmetric distributions where the mean and variance are finite (so the Cauchy, for example, is excluded from consideration).
Further, I'm going to limit myself initially to continuous unimodal cases, and indeed mostly to 'nice' situations (though I might come back later and di... | For what (symmetric) distributions is sample mean a more efficient estimator than sample median? | Let's assume we restrict consideration to symmetric distributions where the mean and variance are finite (so the Cauchy, for example, is excluded from consideration).
Further, I'm going to limit myse | For what (symmetric) distributions is sample mean a more efficient estimator than sample median?
Let's assume we restrict consideration to symmetric distributions where the mean and variance are finite (so the Cauchy, for example, is excluded from consideration).
Further, I'm going to limit myself initially to continu... | For what (symmetric) distributions is sample mean a more efficient estimator than sample median?
Let's assume we restrict consideration to symmetric distributions where the mean and variance are finite (so the Cauchy, for example, is excluded from consideration).
Further, I'm going to limit myse |
14,786 | For what (symmetric) distributions is sample mean a more efficient estimator than sample median? | The median will generally be better than the mean if there are heavy tails, while the mean will be best with light tails. An interesting concrete example is the double exponential (or Laplace) distribution https://en.wikipedia.org/wiki/Laplace_distribution with density function
$$
f(x) = \frac12 e^{-|x-\mu|} , \qu... | For what (symmetric) distributions is sample mean a more efficient estimator than sample median? | The median will generally be better than the mean if there are heavy tails, while the mean will be best with light tails. An interesting concrete example is the double exponential (or Laplace) distri | For what (symmetric) distributions is sample mean a more efficient estimator than sample median?
The median will generally be better than the mean if there are heavy tails, while the mean will be best with light tails. An interesting concrete example is the double exponential (or Laplace) distribution https://en.wikip... | For what (symmetric) distributions is sample mean a more efficient estimator than sample median?
The median will generally be better than the mean if there are heavy tails, while the mean will be best with light tails. An interesting concrete example is the double exponential (or Laplace) distri |
14,787 | Asymptotic consistency with non-zero asymptotic variance - what does it represent? | I won't give a very satisfactory answer to your question because it seems to me to be a little bit too open, but let me try to shed some light on why this question is a hard one.
I think you are struggling with the fact that the conventional topologies we use on probability distributions and random variables are bad. I... | Asymptotic consistency with non-zero asymptotic variance - what does it represent? | I won't give a very satisfactory answer to your question because it seems to me to be a little bit too open, but let me try to shed some light on why this question is a hard one.
I think you are strug | Asymptotic consistency with non-zero asymptotic variance - what does it represent?
I won't give a very satisfactory answer to your question because it seems to me to be a little bit too open, but let me try to shed some light on why this question is a hard one.
I think you are struggling with the fact that the conventi... | Asymptotic consistency with non-zero asymptotic variance - what does it represent?
I won't give a very satisfactory answer to your question because it seems to me to be a little bit too open, but let me try to shed some light on why this question is a hard one.
I think you are strug |
14,788 | Asymptotic consistency with non-zero asymptotic variance - what does it represent? | 27-10-2014: Unfortunately (for me that is), no-one has as yet contributed an answer here -perhaps because it looks like a weird, "pathological" theoretical issue and nothing more?
Well to quote a comment for user Cardinal (which I will subsequently explore)
"Here is an admittedly absurd, but simple example. The idea... | Asymptotic consistency with non-zero asymptotic variance - what does it represent? | 27-10-2014: Unfortunately (for me that is), no-one has as yet contributed an answer here -perhaps because it looks like a weird, "pathological" theoretical issue and nothing more?
Well to quote a co | Asymptotic consistency with non-zero asymptotic variance - what does it represent?
27-10-2014: Unfortunately (for me that is), no-one has as yet contributed an answer here -perhaps because it looks like a weird, "pathological" theoretical issue and nothing more?
Well to quote a comment for user Cardinal (which I will... | Asymptotic consistency with non-zero asymptotic variance - what does it represent?
27-10-2014: Unfortunately (for me that is), no-one has as yet contributed an answer here -perhaps because it looks like a weird, "pathological" theoretical issue and nothing more?
Well to quote a co |
14,789 | Asymptotic consistency with non-zero asymptotic variance - what does it represent? | An estimator is consistent in probability but not in MSE if there is an arbitrarily small probability of the estimator "exploding".
While an interesting mathematical curiosity, for any practical purpose this should not bother you.
For any practical purpose, estimators have finite supports and thus cannot explode (the ... | Asymptotic consistency with non-zero asymptotic variance - what does it represent? | An estimator is consistent in probability but not in MSE if there is an arbitrarily small probability of the estimator "exploding".
While an interesting mathematical curiosity, for any practical purpo | Asymptotic consistency with non-zero asymptotic variance - what does it represent?
An estimator is consistent in probability but not in MSE if there is an arbitrarily small probability of the estimator "exploding".
While an interesting mathematical curiosity, for any practical purpose this should not bother you.
For a... | Asymptotic consistency with non-zero asymptotic variance - what does it represent?
An estimator is consistent in probability but not in MSE if there is an arbitrarily small probability of the estimator "exploding".
While an interesting mathematical curiosity, for any practical purpo |
14,790 | Random Forest can't overfit? | Random forest can overfit. I am sure of this. What is usually meant is that the model would not overfit if you use more trees.
Try for example to estimate the model $y = log(x) + \epsilon$ with a random forest. You will get an almost zero training error but a bad prediction error | Random Forest can't overfit? | Random forest can overfit. I am sure of this. What is usually meant is that the model would not overfit if you use more trees.
Try for example to estimate the model $y = log(x) + \epsilon$ with a ran | Random Forest can't overfit?
Random forest can overfit. I am sure of this. What is usually meant is that the model would not overfit if you use more trees.
Try for example to estimate the model $y = log(x) + \epsilon$ with a random forest. You will get an almost zero training error but a bad prediction error | Random Forest can't overfit?
Random forest can overfit. I am sure of this. What is usually meant is that the model would not overfit if you use more trees.
Try for example to estimate the model $y = log(x) + \epsilon$ with a ran |
14,791 | Random Forest can't overfit? | I will try to give a more thorough answer building on Donbeo's answer and Itachi's comment.
Can Random Forests overfit?
In short, yes, they can.
Why is there a common misconception that Random Forests cannot overfit?
The reason is that, from the outside, the training of Random Forests looks similar to the ones of other... | Random Forest can't overfit? | I will try to give a more thorough answer building on Donbeo's answer and Itachi's comment.
Can Random Forests overfit?
In short, yes, they can.
Why is there a common misconception that Random Forests | Random Forest can't overfit?
I will try to give a more thorough answer building on Donbeo's answer and Itachi's comment.
Can Random Forests overfit?
In short, yes, they can.
Why is there a common misconception that Random Forests cannot overfit?
The reason is that, from the outside, the training of Random Forests looks... | Random Forest can't overfit?
I will try to give a more thorough answer building on Donbeo's answer and Itachi's comment.
Can Random Forests overfit?
In short, yes, they can.
Why is there a common misconception that Random Forests |
14,792 | Random Forest can't overfit? | Hastie et al. address this question very briefly in Elements of Statistical Learning (page 596).
Another claim is that random forests “cannot overfit” the data. It is certainly true that increasing $\mathcal{B}$ [the number of trees in the ensemble] does not cause the random forest sequence to overfit... However, this... | Random Forest can't overfit? | Hastie et al. address this question very briefly in Elements of Statistical Learning (page 596).
Another claim is that random forests “cannot overfit” the data. It is certainly true that increasing $ | Random Forest can't overfit?
Hastie et al. address this question very briefly in Elements of Statistical Learning (page 596).
Another claim is that random forests “cannot overfit” the data. It is certainly true that increasing $\mathcal{B}$ [the number of trees in the ensemble] does not cause the random forest sequenc... | Random Forest can't overfit?
Hastie et al. address this question very briefly in Elements of Statistical Learning (page 596).
Another claim is that random forests “cannot overfit” the data. It is certainly true that increasing $ |
14,793 | Statistical test for two distributions where only 5-number summary is known? | Under the null hypothesis that the distributions are the same and both samples are obtained randomly and independently from the common distribution, we can work out the sizes of all $5\times 5$ (deterministic) tests that can be made by comparing one letter value to another. Some of these tests appear to have reasonabl... | Statistical test for two distributions where only 5-number summary is known? | Under the null hypothesis that the distributions are the same and both samples are obtained randomly and independently from the common distribution, we can work out the sizes of all $5\times 5$ (deter | Statistical test for two distributions where only 5-number summary is known?
Under the null hypothesis that the distributions are the same and both samples are obtained randomly and independently from the common distribution, we can work out the sizes of all $5\times 5$ (deterministic) tests that can be made by compari... | Statistical test for two distributions where only 5-number summary is known?
Under the null hypothesis that the distributions are the same and both samples are obtained randomly and independently from the common distribution, we can work out the sizes of all $5\times 5$ (deter |
14,794 | Statistical test for two distributions where only 5-number summary is known? | I'm pretty confident there isn't going to be one already in the literature, but if you seek a nonparametric test, it would have to be under the assumption of continuity of the underlying variable --- you could look at something like an ECDF-type statistic - say some equivalent to a Kolmogorov-Smirnov-type statistic or... | Statistical test for two distributions where only 5-number summary is known? | I'm pretty confident there isn't going to be one already in the literature, but if you seek a nonparametric test, it would have to be under the assumption of continuity of the underlying variable --- | Statistical test for two distributions where only 5-number summary is known?
I'm pretty confident there isn't going to be one already in the literature, but if you seek a nonparametric test, it would have to be under the assumption of continuity of the underlying variable --- you could look at something like an ECDF-t... | Statistical test for two distributions where only 5-number summary is known?
I'm pretty confident there isn't going to be one already in the literature, but if you seek a nonparametric test, it would have to be under the assumption of continuity of the underlying variable --- |
14,795 | Statistical test for two distributions where only 5-number summary is known? | I don't see how there could be such a test, at least without some assumptions.
You can have two different distributions that have the same 5 number summary:
Here is a trivial example, where I change only 2 numbers, but clearly more numbers could be changed
set.seed(123)
#Create data
x <- rnorm(1000)
... | Statistical test for two distributions where only 5-number summary is known? | I don't see how there could be such a test, at least without some assumptions.
You can have two different distributions that have the same 5 number summary:
Here is a trivial example, where I change o | Statistical test for two distributions where only 5-number summary is known?
I don't see how there could be such a test, at least without some assumptions.
You can have two different distributions that have the same 5 number summary:
Here is a trivial example, where I change only 2 numbers, but clearly more numbers cou... | Statistical test for two distributions where only 5-number summary is known?
I don't see how there could be such a test, at least without some assumptions.
You can have two different distributions that have the same 5 number summary:
Here is a trivial example, where I change o |
14,796 | Regularized bayesian logistic regression in JAGS | Since L1 regularization is equivalent to a Laplace (double exponential) prior on the relevant coefficients, you can do it as follows. Here I have three independent variables x1, x2, and x3, and y is the binary target variable. Selection of the regularization parameter $\lambda$ is done here by putting a hyperprior on... | Regularized bayesian logistic regression in JAGS | Since L1 regularization is equivalent to a Laplace (double exponential) prior on the relevant coefficients, you can do it as follows. Here I have three independent variables x1, x2, and x3, and y is | Regularized bayesian logistic regression in JAGS
Since L1 regularization is equivalent to a Laplace (double exponential) prior on the relevant coefficients, you can do it as follows. Here I have three independent variables x1, x2, and x3, and y is the binary target variable. Selection of the regularization parameter ... | Regularized bayesian logistic regression in JAGS
Since L1 regularization is equivalent to a Laplace (double exponential) prior on the relevant coefficients, you can do it as follows. Here I have three independent variables x1, x2, and x3, and y is |
14,797 | Interpretation of incidence-rate ratios | Ah, the incident rate ratio, my old friend.
You're correct. If we have a 0/1 variable, an IRR of 0.7 means that those with X = 1 will have 0.7 times the incident events as those with X = 0. If you want the actual number of predicted counts, you'll have to back-track to the unexponentiated model coefficients. Then your... | Interpretation of incidence-rate ratios | Ah, the incident rate ratio, my old friend.
You're correct. If we have a 0/1 variable, an IRR of 0.7 means that those with X = 1 will have 0.7 times the incident events as those with X = 0. If you wa | Interpretation of incidence-rate ratios
Ah, the incident rate ratio, my old friend.
You're correct. If we have a 0/1 variable, an IRR of 0.7 means that those with X = 1 will have 0.7 times the incident events as those with X = 0. If you want the actual number of predicted counts, you'll have to back-track to the unexp... | Interpretation of incidence-rate ratios
Ah, the incident rate ratio, my old friend.
You're correct. If we have a 0/1 variable, an IRR of 0.7 means that those with X = 1 will have 0.7 times the incident events as those with X = 0. If you wa |
14,798 | Interpretation of incidence-rate ratios | Yes, that sounds about right: to be precise, the expected count is multiplied by a factor of .7 when the independent variable increases by one unit.
The term 'incidence rate ratio" assumes that you're fitting a model with an exposure() (offset) term as well, typically specifying the time each unit was observed for, in ... | Interpretation of incidence-rate ratios | Yes, that sounds about right: to be precise, the expected count is multiplied by a factor of .7 when the independent variable increases by one unit.
The term 'incidence rate ratio" assumes that you're | Interpretation of incidence-rate ratios
Yes, that sounds about right: to be precise, the expected count is multiplied by a factor of .7 when the independent variable increases by one unit.
The term 'incidence rate ratio" assumes that you're fitting a model with an exposure() (offset) term as well, typically specifying ... | Interpretation of incidence-rate ratios
Yes, that sounds about right: to be precise, the expected count is multiplied by a factor of .7 when the independent variable increases by one unit.
The term 'incidence rate ratio" assumes that you're |
14,799 | Logic behind the ANOVA F-test in simple linear regression | In the simplest case, when you have only one predictor (simple regression), say $X_1$, the $F$-test tells you whether including $X_1$ does explain a larger part of the variance observed in $Y$ compared to the null model (intercept only). The idea is then to test if the added explained variance (total variance, TSS, min... | Logic behind the ANOVA F-test in simple linear regression | In the simplest case, when you have only one predictor (simple regression), say $X_1$, the $F$-test tells you whether including $X_1$ does explain a larger part of the variance observed in $Y$ compare | Logic behind the ANOVA F-test in simple linear regression
In the simplest case, when you have only one predictor (simple regression), say $X_1$, the $F$-test tells you whether including $X_1$ does explain a larger part of the variance observed in $Y$ compared to the null model (intercept only). The idea is then to test... | Logic behind the ANOVA F-test in simple linear regression
In the simplest case, when you have only one predictor (simple regression), say $X_1$, the $F$-test tells you whether including $X_1$ does explain a larger part of the variance observed in $Y$ compare |
14,800 | Logit with ordinal independent variables | To add to @dmk38's response, "any set of scores gives a valid test, provided they are constructed without consulting the results of the experiment. If the set of scores is poor, in that it badly distorts a numerical scale that really does underlie the ordered classification, the test will not be sensitive. The scores s... | Logit with ordinal independent variables | To add to @dmk38's response, "any set of scores gives a valid test, provided they are constructed without consulting the results of the experiment. If the set of scores is poor, in that it badly disto | Logit with ordinal independent variables
To add to @dmk38's response, "any set of scores gives a valid test, provided they are constructed without consulting the results of the experiment. If the set of scores is poor, in that it badly distorts a numerical scale that really does underlie the ordered classification, the... | Logit with ordinal independent variables
To add to @dmk38's response, "any set of scores gives a valid test, provided they are constructed without consulting the results of the experiment. If the set of scores is poor, in that it badly disto |
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