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 |
|---|---|---|---|---|---|---|
10,001 | Difference between samples, time steps and features in neural network | I found this just below the [samples, time_steps, features] you are concerned with.
X = numpy.reshape(dataX, (len(dataX), seq_length, 1))
Samples - This is the len(dataX), or the amount of data points you have.
Time steps - This is equivalent to the amount of time steps you run your recurrent neural network. If you w... | Difference between samples, time steps and features in neural network | I found this just below the [samples, time_steps, features] you are concerned with.
X = numpy.reshape(dataX, (len(dataX), seq_length, 1))
Samples - This is the len(dataX), or the amount of data poin | Difference between samples, time steps and features in neural network
I found this just below the [samples, time_steps, features] you are concerned with.
X = numpy.reshape(dataX, (len(dataX), seq_length, 1))
Samples - This is the len(dataX), or the amount of data points you have.
Time steps - This is equivalent to th... | Difference between samples, time steps and features in neural network
I found this just below the [samples, time_steps, features] you are concerned with.
X = numpy.reshape(dataX, (len(dataX), seq_length, 1))
Samples - This is the len(dataX), or the amount of data poin |
10,002 | Difference between samples, time steps and features in neural network | My answer with an example: ["hello this is xyz","how are you doing","great man..."]
in this case "[samples, time steps, features]" means:
sample: 3 because there are 3 elements in the list
time steps: here you can take max_length = 4
length("hello this is xyz") = 4; length("how are you doing") = 4; length("great
m... | Difference between samples, time steps and features in neural network | My answer with an example: ["hello this is xyz","how are you doing","great man..."]
in this case "[samples, time steps, features]" means:
sample: 3 because there are 3 elements in the list
time step | Difference between samples, time steps and features in neural network
My answer with an example: ["hello this is xyz","how are you doing","great man..."]
in this case "[samples, time steps, features]" means:
sample: 3 because there are 3 elements in the list
time steps: here you can take max_length = 4
length("hell... | Difference between samples, time steps and features in neural network
My answer with an example: ["hello this is xyz","how are you doing","great man..."]
in this case "[samples, time steps, features]" means:
sample: 3 because there are 3 elements in the list
time step |
10,003 | Difference between samples, time steps and features in neural network | It's a bit too late but just in case;
A Sample may refer to individual training examples. A “batch_size” variable is hence the count of samples you sent to the neural network. That is, how many different examples you feed at once to the neural network.
TimeSteps are ticks of time. It is how long in time each of your sa... | Difference between samples, time steps and features in neural network | It's a bit too late but just in case;
A Sample may refer to individual training examples. A “batch_size” variable is hence the count of samples you sent to the neural network. That is, how many differ | Difference between samples, time steps and features in neural network
It's a bit too late but just in case;
A Sample may refer to individual training examples. A “batch_size” variable is hence the count of samples you sent to the neural network. That is, how many different examples you feed at once to the neural networ... | Difference between samples, time steps and features in neural network
It's a bit too late but just in case;
A Sample may refer to individual training examples. A “batch_size” variable is hence the count of samples you sent to the neural network. That is, how many differ |
10,004 | What exactly is called "principal component" in PCA? | You are absolutely correct in observing that even though $\mathbf{u}$ (one of the eigenvectors of the covariance matrix, e.g. the first one) and $\mathbf{X}\mathbf{u}$ (projection of the data onto the 1-dimensional subspace spanned by $\mathbf{u}$) are two different things, both of them are often called "principal comp... | What exactly is called "principal component" in PCA? | You are absolutely correct in observing that even though $\mathbf{u}$ (one of the eigenvectors of the covariance matrix, e.g. the first one) and $\mathbf{X}\mathbf{u}$ (projection of the data onto the | What exactly is called "principal component" in PCA?
You are absolutely correct in observing that even though $\mathbf{u}$ (one of the eigenvectors of the covariance matrix, e.g. the first one) and $\mathbf{X}\mathbf{u}$ (projection of the data onto the 1-dimensional subspace spanned by $\mathbf{u}$) are two different ... | What exactly is called "principal component" in PCA?
You are absolutely correct in observing that even though $\mathbf{u}$ (one of the eigenvectors of the covariance matrix, e.g. the first one) and $\mathbf{X}\mathbf{u}$ (projection of the data onto the |
10,005 | probability of one random variable being greater than another | This question is now old enough that I can give you a solution without ruining your homework. As you have pointed out in your question, to compute this probability, you need to find the distribution of $D=X-Y$. One of the properties of the normal distribution is that any linear combination of independent normal rando... | probability of one random variable being greater than another | This question is now old enough that I can give you a solution without ruining your homework. As you have pointed out in your question, to compute this probability, you need to find the distribution | probability of one random variable being greater than another
This question is now old enough that I can give you a solution without ruining your homework. As you have pointed out in your question, to compute this probability, you need to find the distribution of $D=X-Y$. One of the properties of the normal distribut... | probability of one random variable being greater than another
This question is now old enough that I can give you a solution without ruining your homework. As you have pointed out in your question, to compute this probability, you need to find the distribution |
10,006 | probability of one random variable being greater than another | Ok, since this is homework, you get hints instead if straight answers.
Rather than thinking about $P(X>Y)$ why not think about $P(X-Y>0)$. This is clearly the same probability yes? So now you just need to work out the distribution of $Z=X-Y$
Do you know how to do that?
Edit
Ok, so your problem is with the distributio... | probability of one random variable being greater than another | Ok, since this is homework, you get hints instead if straight answers.
Rather than thinking about $P(X>Y)$ why not think about $P(X-Y>0)$. This is clearly the same probability yes? So now you just n | probability of one random variable being greater than another
Ok, since this is homework, you get hints instead if straight answers.
Rather than thinking about $P(X>Y)$ why not think about $P(X-Y>0)$. This is clearly the same probability yes? So now you just need to work out the distribution of $Z=X-Y$
Do you know ho... | probability of one random variable being greater than another
Ok, since this is homework, you get hints instead if straight answers.
Rather than thinking about $P(X>Y)$ why not think about $P(X-Y>0)$. This is clearly the same probability yes? So now you just n |
10,007 | probability of one random variable being greater than another | $D=X-Y$ is normal with mean $-1$ and variance $2+3$.
Knowing the distribution of $D$, you can calculate required probability. | probability of one random variable being greater than another | $D=X-Y$ is normal with mean $-1$ and variance $2+3$.
Knowing the distribution of $D$, you can calculate required probability. | probability of one random variable being greater than another
$D=X-Y$ is normal with mean $-1$ and variance $2+3$.
Knowing the distribution of $D$, you can calculate required probability. | probability of one random variable being greater than another
$D=X-Y$ is normal with mean $-1$ and variance $2+3$.
Knowing the distribution of $D$, you can calculate required probability. |
10,008 | probability of one random variable being greater than another | I think this should work.
In general, suppose $X$ has distribution function $\mathbb{G}(x)$, and $Y$ has distribution function $\mathbb{H}(x)$ and $X$ and $Y$ are independent. We need to find the probability $P(X>Y)$.
Now, for some constant $\alpha$, $P(Y<\alpha)=\mathbb{H}(\alpha)$. Therefore, $P(Y<X)=\int_{x}\mathb... | probability of one random variable being greater than another | I think this should work.
In general, suppose $X$ has distribution function $\mathbb{G}(x)$, and $Y$ has distribution function $\mathbb{H}(x)$ and $X$ and $Y$ are independent. We need to find the pro | probability of one random variable being greater than another
I think this should work.
In general, suppose $X$ has distribution function $\mathbb{G}(x)$, and $Y$ has distribution function $\mathbb{H}(x)$ and $X$ and $Y$ are independent. We need to find the probability $P(X>Y)$.
Now, for some constant $\alpha$, $P(Y<... | probability of one random variable being greater than another
I think this should work.
In general, suppose $X$ has distribution function $\mathbb{G}(x)$, and $Y$ has distribution function $\mathbb{H}(x)$ and $X$ and $Y$ are independent. We need to find the pro |
10,009 | What does it mean that AUC is a semi-proper scoring rule? | Let's start with an example. Say Alice is a track coach and wants to pick an athlete to represent the team in an upcoming sporting event, a 200m sprint. Naturally she wants to pick the fastest runner.
A strictly proper scoring rule would be to nominate the fastest runner of the team over the 200m distance. This maximi... | What does it mean that AUC is a semi-proper scoring rule? | Let's start with an example. Say Alice is a track coach and wants to pick an athlete to represent the team in an upcoming sporting event, a 200m sprint. Naturally she wants to pick the fastest runner. | What does it mean that AUC is a semi-proper scoring rule?
Let's start with an example. Say Alice is a track coach and wants to pick an athlete to represent the team in an upcoming sporting event, a 200m sprint. Naturally she wants to pick the fastest runner.
A strictly proper scoring rule would be to nominate the fast... | What does it mean that AUC is a semi-proper scoring rule?
Let's start with an example. Say Alice is a track coach and wants to pick an athlete to represent the team in an upcoming sporting event, a 200m sprint. Naturally she wants to pick the fastest runner. |
10,010 | Are line search methods used in deep learning? Why not? | Vanilla gradient descent can be made more reliable using line searches; I've written algorithms that do this and it makes for a very stable algorithm (although not necessarily fast).
However, it makes almost no sense to do a line search for stochastic gradient methods. The reason I say this is that if we do a line sea... | Are line search methods used in deep learning? Why not? | Vanilla gradient descent can be made more reliable using line searches; I've written algorithms that do this and it makes for a very stable algorithm (although not necessarily fast).
However, it make | Are line search methods used in deep learning? Why not?
Vanilla gradient descent can be made more reliable using line searches; I've written algorithms that do this and it makes for a very stable algorithm (although not necessarily fast).
However, it makes almost no sense to do a line search for stochastic gradient me... | Are line search methods used in deep learning? Why not?
Vanilla gradient descent can be made more reliable using line searches; I've written algorithms that do this and it makes for a very stable algorithm (although not necessarily fast).
However, it make |
10,011 | Are line search methods used in deep learning? Why not? | The tutorials talk about gradient descent presumably because it is one of the simplest algorithms used for optimization, so it is easy to explain. Since most of such tutorials are rather brief, they focus on simple stuff. There are at least several popular optimization algorithms beyond simple gradient descent that are... | Are line search methods used in deep learning? Why not? | The tutorials talk about gradient descent presumably because it is one of the simplest algorithms used for optimization, so it is easy to explain. Since most of such tutorials are rather brief, they f | Are line search methods used in deep learning? Why not?
The tutorials talk about gradient descent presumably because it is one of the simplest algorithms used for optimization, so it is easy to explain. Since most of such tutorials are rather brief, they focus on simple stuff. There are at least several popular optimiz... | Are line search methods used in deep learning? Why not?
The tutorials talk about gradient descent presumably because it is one of the simplest algorithms used for optimization, so it is easy to explain. Since most of such tutorials are rather brief, they f |
10,012 | Are line search methods used in deep learning? Why not? | This question was asked in early 2018, but if you still wait for an answer then: Yes, now there are some implementation of line search in DNN with good performances. See:
https://arxiv.org/abs/1808.05160 (published in 2 journals)
and more recently, by a different group:
https://arxiv.org/abs/1905.09997 (published in IC... | Are line search methods used in deep learning? Why not? | This question was asked in early 2018, but if you still wait for an answer then: Yes, now there are some implementation of line search in DNN with good performances. See:
https://arxiv.org/abs/1808.05 | Are line search methods used in deep learning? Why not?
This question was asked in early 2018, but if you still wait for an answer then: Yes, now there are some implementation of line search in DNN with good performances. See:
https://arxiv.org/abs/1808.05160 (published in 2 journals)
and more recently, by a different ... | Are line search methods used in deep learning? Why not?
This question was asked in early 2018, but if you still wait for an answer then: Yes, now there are some implementation of line search in DNN with good performances. See:
https://arxiv.org/abs/1808.05 |
10,013 | Choice between Type-I, Type-II, or Type-III ANOVA [duplicate] | I realize this is a year old post but its likely to come up again.
There are many factors that play into this, I'd argue the most important is your hypothesis. So there is no clear answer but I generally follow these rules-of-thumb:
1) Type II is only when you don't have an interaction term.
2) Type I vs Type III to te... | Choice between Type-I, Type-II, or Type-III ANOVA [duplicate] | I realize this is a year old post but its likely to come up again.
There are many factors that play into this, I'd argue the most important is your hypothesis. So there is no clear answer but I genera | Choice between Type-I, Type-II, or Type-III ANOVA [duplicate]
I realize this is a year old post but its likely to come up again.
There are many factors that play into this, I'd argue the most important is your hypothesis. So there is no clear answer but I generally follow these rules-of-thumb:
1) Type II is only when y... | Choice between Type-I, Type-II, or Type-III ANOVA [duplicate]
I realize this is a year old post but its likely to come up again.
There are many factors that play into this, I'd argue the most important is your hypothesis. So there is no clear answer but I genera |
10,014 | Choice between Type-I, Type-II, or Type-III ANOVA [duplicate] | I've never been a fan of Type III SS for ANOVA's so this is a biased recommendation.
I believe you should select Type II in this case. In the Type I ANOVA order matters. So, whether you include iV1, or iV2 first makes a difference because the first (e.g. iV1) is compared to a model with just an intercept while the se... | Choice between Type-I, Type-II, or Type-III ANOVA [duplicate] | I've never been a fan of Type III SS for ANOVA's so this is a biased recommendation.
I believe you should select Type II in this case. In the Type I ANOVA order matters. So, whether you include iV1, | Choice between Type-I, Type-II, or Type-III ANOVA [duplicate]
I've never been a fan of Type III SS for ANOVA's so this is a biased recommendation.
I believe you should select Type II in this case. In the Type I ANOVA order matters. So, whether you include iV1, or iV2 first makes a difference because the first (e.g. i... | Choice between Type-I, Type-II, or Type-III ANOVA [duplicate]
I've never been a fan of Type III SS for ANOVA's so this is a biased recommendation.
I believe you should select Type II in this case. In the Type I ANOVA order matters. So, whether you include iV1, |
10,015 | Choice between Type-I, Type-II, or Type-III ANOVA [duplicate] | I have recently made a decision : I will never use again an ANOVA sum-of-squares testing, except for the interaction. Why ?
Because, in general, the hypothesis $H_0$ of the tests of the main effects are diffcult to interpet.
Because we can do something really more instructive and interpretable: multiple comparisons w... | Choice between Type-I, Type-II, or Type-III ANOVA [duplicate] | I have recently made a decision : I will never use again an ANOVA sum-of-squares testing, except for the interaction. Why ?
Because, in general, the hypothesis $H_0$ of the tests of the main effects | Choice between Type-I, Type-II, or Type-III ANOVA [duplicate]
I have recently made a decision : I will never use again an ANOVA sum-of-squares testing, except for the interaction. Why ?
Because, in general, the hypothesis $H_0$ of the tests of the main effects are diffcult to interpet.
Because we can do something rea... | Choice between Type-I, Type-II, or Type-III ANOVA [duplicate]
I have recently made a decision : I will never use again an ANOVA sum-of-squares testing, except for the interaction. Why ?
Because, in general, the hypothesis $H_0$ of the tests of the main effects |
10,016 | How to estimate how many people attended an event (say, a political rally)? | You could estimate the people per square meter (use a few areas, of at least a few square meters each to get a good estimate) and multiply this by the size of the area.
Here is an article on this topic: How is Crowd Size estimated? | How to estimate how many people attended an event (say, a political rally)? | You could estimate the people per square meter (use a few areas, of at least a few square meters each to get a good estimate) and multiply this by the size of the area.
Here is an article on this topi | How to estimate how many people attended an event (say, a political rally)?
You could estimate the people per square meter (use a few areas, of at least a few square meters each to get a good estimate) and multiply this by the size of the area.
Here is an article on this topic: How is Crowd Size estimated? | How to estimate how many people attended an event (say, a political rally)?
You could estimate the people per square meter (use a few areas, of at least a few square meters each to get a good estimate) and multiply this by the size of the area.
Here is an article on this topi |
10,017 | How to estimate how many people attended an event (say, a political rally)? | As a follow up to this question (thanks to all for the answers), Significance magazine just published an article about this very question (it's the cover story, to boot): "How many were there when it mattered? Estimating the sizes of crowds." September 2011, volume 8, issue 3, pages 104-107, by Ray Watson and Paul Yi... | How to estimate how many people attended an event (say, a political rally)? | As a follow up to this question (thanks to all for the answers), Significance magazine just published an article about this very question (it's the cover story, to boot): "How many were there when it | How to estimate how many people attended an event (say, a political rally)?
As a follow up to this question (thanks to all for the answers), Significance magazine just published an article about this very question (it's the cover story, to boot): "How many were there when it mattered? Estimating the sizes of crowds." ... | How to estimate how many people attended an event (say, a political rally)?
As a follow up to this question (thanks to all for the answers), Significance magazine just published an article about this very question (it's the cover story, to boot): "How many were there when it |
10,018 | How to estimate how many people attended an event (say, a political rally)? | Tim's linked article is great, though I think the company that counts people in grids is making it out to be easier than it really is.
In the local (DC) papers, I've seen quotes about Metro rider usage (except there were two other major events downtown the same day), attempts to count people at security checkpoints, gr... | How to estimate how many people attended an event (say, a political rally)? | Tim's linked article is great, though I think the company that counts people in grids is making it out to be easier than it really is.
In the local (DC) papers, I've seen quotes about Metro rider usag | How to estimate how many people attended an event (say, a political rally)?
Tim's linked article is great, though I think the company that counts people in grids is making it out to be easier than it really is.
In the local (DC) papers, I've seen quotes about Metro rider usage (except there were two other major events ... | How to estimate how many people attended an event (say, a political rally)?
Tim's linked article is great, though I think the company that counts people in grids is making it out to be easier than it really is.
In the local (DC) papers, I've seen quotes about Metro rider usag |
10,019 | How to estimate how many people attended an event (say, a political rally)? | Mobile phone providers can count the number of phones in the area. Having an estimate of the mean number of phones/person good approximation can be calculated. This looks simple, so I assume it is in practice. | How to estimate how many people attended an event (say, a political rally)? | Mobile phone providers can count the number of phones in the area. Having an estimate of the mean number of phones/person good approximation can be calculated. This looks simple, so I assume it is in | How to estimate how many people attended an event (say, a political rally)?
Mobile phone providers can count the number of phones in the area. Having an estimate of the mean number of phones/person good approximation can be calculated. This looks simple, so I assume it is in practice. | How to estimate how many people attended an event (say, a political rally)?
Mobile phone providers can count the number of phones in the area. Having an estimate of the mean number of phones/person good approximation can be calculated. This looks simple, so I assume it is in |
10,020 | How to estimate how many people attended an event (say, a political rally)? | There are companies that specialize in counting people. For instance,
www.lynce.es$^\dagger$
(I am not affiliated nor have any interest whatsoever in such company).
They hung cameras over the groups they want to count, shoot pictures
and actually count heads. They only make small adjustments when it comes to estimate p... | How to estimate how many people attended an event (say, a political rally)? | There are companies that specialize in counting people. For instance,
www.lynce.es$^\dagger$
(I am not affiliated nor have any interest whatsoever in such company).
They hung cameras over the groups t | How to estimate how many people attended an event (say, a political rally)?
There are companies that specialize in counting people. For instance,
www.lynce.es$^\dagger$
(I am not affiliated nor have any interest whatsoever in such company).
They hung cameras over the groups they want to count, shoot pictures
and actual... | How to estimate how many people attended an event (say, a political rally)?
There are companies that specialize in counting people. For instance,
www.lynce.es$^\dagger$
(I am not affiliated nor have any interest whatsoever in such company).
They hung cameras over the groups t |
10,021 | How to estimate how many people attended an event (say, a political rally)? | As an alternative to WiFi mentioned by Uri, you could place Bluetooth scanner(s) in 'strategic' locations of your venue. I've attended a presentation during MPA workshop about such development in Netherlands. | How to estimate how many people attended an event (say, a political rally)? | As an alternative to WiFi mentioned by Uri, you could place Bluetooth scanner(s) in 'strategic' locations of your venue. I've attended a presentation during MPA workshop about such development in Neth | How to estimate how many people attended an event (say, a political rally)?
As an alternative to WiFi mentioned by Uri, you could place Bluetooth scanner(s) in 'strategic' locations of your venue. I've attended a presentation during MPA workshop about such development in Netherlands. | How to estimate how many people attended an event (say, a political rally)?
As an alternative to WiFi mentioned by Uri, you could place Bluetooth scanner(s) in 'strategic' locations of your venue. I've attended a presentation during MPA workshop about such development in Neth |
10,022 | How to estimate how many people attended an event (say, a political rally)? | Here's an idea (but I am not sure this could work in practice): place a free wifi access point, and count the number of connections ( of iPhones, blackbery...). | How to estimate how many people attended an event (say, a political rally)? | Here's an idea (but I am not sure this could work in practice): place a free wifi access point, and count the number of connections ( of iPhones, blackbery...). | How to estimate how many people attended an event (say, a political rally)?
Here's an idea (but I am not sure this could work in practice): place a free wifi access point, and count the number of connections ( of iPhones, blackbery...). | How to estimate how many people attended an event (say, a political rally)?
Here's an idea (but I am not sure this could work in practice): place a free wifi access point, and count the number of connections ( of iPhones, blackbery...). |
10,023 | How to estimate how many people attended an event (say, a political rally)? | A police officer told me once that they had rules of thumb to guesstimate attendance at demonstrations (don't ask me for specifics), probably based on what Tim said. | How to estimate how many people attended an event (say, a political rally)? | A police officer told me once that they had rules of thumb to guesstimate attendance at demonstrations (don't ask me for specifics), probably based on what Tim said. | How to estimate how many people attended an event (say, a political rally)?
A police officer told me once that they had rules of thumb to guesstimate attendance at demonstrations (don't ask me for specifics), probably based on what Tim said. | How to estimate how many people attended an event (say, a political rally)?
A police officer told me once that they had rules of thumb to guesstimate attendance at demonstrations (don't ask me for specifics), probably based on what Tim said. |
10,024 | Any suggestions for making R code use multiple processors? | If it's on Linux, then the most straight-forward is multicore. Beyond that, I suggest having a look at MPI (especially with the snow package).
More generally, have a look at:
The High-Performance Computing view on CRAN.
"State of the Art in Parallel Computing with R"
Lastly, I recommend using the foreach package to ... | Any suggestions for making R code use multiple processors? | If it's on Linux, then the most straight-forward is multicore. Beyond that, I suggest having a look at MPI (especially with the snow package).
More generally, have a look at:
The High-Performance Co | Any suggestions for making R code use multiple processors?
If it's on Linux, then the most straight-forward is multicore. Beyond that, I suggest having a look at MPI (especially with the snow package).
More generally, have a look at:
The High-Performance Computing view on CRAN.
"State of the Art in Parallel Computing... | Any suggestions for making R code use multiple processors?
If it's on Linux, then the most straight-forward is multicore. Beyond that, I suggest having a look at MPI (especially with the snow package).
More generally, have a look at:
The High-Performance Co |
10,025 | Any suggestions for making R code use multiple processors? | If you are using GNU/Linux previous answers by Shane and Dirk are great.
If you need a solution for windows, there is one in this post:
Parallel Multicore Processing with R (on Windows)
Although the package is not yet on CRAN. it can be downloaded from that link. | Any suggestions for making R code use multiple processors? | If you are using GNU/Linux previous answers by Shane and Dirk are great.
If you need a solution for windows, there is one in this post:
Parallel Multicore Processing with R (on Windows)
Although the p | Any suggestions for making R code use multiple processors?
If you are using GNU/Linux previous answers by Shane and Dirk are great.
If you need a solution for windows, there is one in this post:
Parallel Multicore Processing with R (on Windows)
Although the package is not yet on CRAN. it can be downloaded from that lin... | Any suggestions for making R code use multiple processors?
If you are using GNU/Linux previous answers by Shane and Dirk are great.
If you need a solution for windows, there is one in this post:
Parallel Multicore Processing with R (on Windows)
Although the p |
10,026 | Any suggestions for making R code use multiple processors? | Shane is correct. Both multicore and Rmpi are winners.
Slightly broader coverage of the topic is in the CRAN Task View on High-Performance Computing. This also links to a fairly recent survey article on Parallel Computing with R from JSS.
Lastly, a few hands-on examples and tips are in the Intro to HPC with R tutori... | Any suggestions for making R code use multiple processors? | Shane is correct. Both multicore and Rmpi are winners.
Slightly broader coverage of the topic is in the CRAN Task View on High-Performance Computing. This also links to a fairly recent survey article | Any suggestions for making R code use multiple processors?
Shane is correct. Both multicore and Rmpi are winners.
Slightly broader coverage of the topic is in the CRAN Task View on High-Performance Computing. This also links to a fairly recent survey article on Parallel Computing with R from JSS.
Lastly, a few hands... | Any suggestions for making R code use multiple processors?
Shane is correct. Both multicore and Rmpi are winners.
Slightly broader coverage of the topic is in the CRAN Task View on High-Performance Computing. This also links to a fairly recent survey article |
10,027 | Any suggestions for making R code use multiple processors? | I noticed that the previous answers lack some general HPC considerations.
First of all, neither of those packages will enable you to run one SVM in parallel. So what you can speed up is parameter optimization or cross-validation, still you must write your own functions for that. Or of course you may run the job for dif... | Any suggestions for making R code use multiple processors? | I noticed that the previous answers lack some general HPC considerations.
First of all, neither of those packages will enable you to run one SVM in parallel. So what you can speed up is parameter opti | Any suggestions for making R code use multiple processors?
I noticed that the previous answers lack some general HPC considerations.
First of all, neither of those packages will enable you to run one SVM in parallel. So what you can speed up is parameter optimization or cross-validation, still you must write your own f... | Any suggestions for making R code use multiple processors?
I noticed that the previous answers lack some general HPC considerations.
First of all, neither of those packages will enable you to run one SVM in parallel. So what you can speed up is parameter opti |
10,028 | Any suggestions for making R code use multiple processors? | Both Shane and Dirk's responses are spot on.
Nevertheless, you might wanna take a look at a commercial version of R, called Revolution R which is built to deal with big datasets and run on multiple cores. This software is free for academics (which might be your case, I dont know) | Any suggestions for making R code use multiple processors? | Both Shane and Dirk's responses are spot on.
Nevertheless, you might wanna take a look at a commercial version of R, called Revolution R which is built to deal with big datasets and run on multiple | Any suggestions for making R code use multiple processors?
Both Shane and Dirk's responses are spot on.
Nevertheless, you might wanna take a look at a commercial version of R, called Revolution R which is built to deal with big datasets and run on multiple cores. This software is free for academics (which might be yo... | Any suggestions for making R code use multiple processors?
Both Shane and Dirk's responses are spot on.
Nevertheless, you might wanna take a look at a commercial version of R, called Revolution R which is built to deal with big datasets and run on multiple |
10,029 | What's the intuition behind contrastive learning or approach? | Contrastive learning is very intuitive. If I ask you to find the matching animal in the photo below, you can do so quite easily. You understand the animal on left is a "cat" and you want to find another "cat" image on the right side. So, you can contrast between similar and dissimilar things.
Contrastive learning i... | What's the intuition behind contrastive learning or approach? | Contrastive learning is very intuitive. If I ask you to find the matching animal in the photo below, you can do so quite easily. You understand the animal on left is a "cat" and you want to find anoth | What's the intuition behind contrastive learning or approach?
Contrastive learning is very intuitive. If I ask you to find the matching animal in the photo below, you can do so quite easily. You understand the animal on left is a "cat" and you want to find another "cat" image on the right side. So, you can contrast bet... | What's the intuition behind contrastive learning or approach?
Contrastive learning is very intuitive. If I ask you to find the matching animal in the photo below, you can do so quite easily. You understand the animal on left is a "cat" and you want to find anoth |
10,030 | What's the intuition behind contrastive learning or approach? | Contrastive learning is a framework that learns similar/dissimilar representations from data that are organized into similar/dissimilar pairs. This can be formulated as a dictionary look-up problem.
Both MoCo and SimCLR use varients of a contrastive loss
function, like InfoNCE from the paper Representation Learning wit... | What's the intuition behind contrastive learning or approach? | Contrastive learning is a framework that learns similar/dissimilar representations from data that are organized into similar/dissimilar pairs. This can be formulated as a dictionary look-up problem.
B | What's the intuition behind contrastive learning or approach?
Contrastive learning is a framework that learns similar/dissimilar representations from data that are organized into similar/dissimilar pairs. This can be formulated as a dictionary look-up problem.
Both MoCo and SimCLR use varients of a contrastive loss
fun... | What's the intuition behind contrastive learning or approach?
Contrastive learning is a framework that learns similar/dissimilar representations from data that are organized into similar/dissimilar pairs. This can be formulated as a dictionary look-up problem.
B |
10,031 | Adding weights to logistic regression for imbalanced data | Ching, You do not have to make your data set balanced in terms of 1’s and 0’s. All you need is sufficient number of 1’s for the maximum likelihood to converge. Looking at the distribution of 1’s (100,000) in your dataset, you should not have any problems. You can do a simple experiment here
Sample 10 % of the 1’s an... | Adding weights to logistic regression for imbalanced data | Ching, You do not have to make your data set balanced in terms of 1’s and 0’s. All you need is sufficient number of 1’s for the maximum likelihood to converge. Looking at the distribution of 1’s (10 | Adding weights to logistic regression for imbalanced data
Ching, You do not have to make your data set balanced in terms of 1’s and 0’s. All you need is sufficient number of 1’s for the maximum likelihood to converge. Looking at the distribution of 1’s (100,000) in your dataset, you should not have any problems. You ... | Adding weights to logistic regression for imbalanced data
Ching, You do not have to make your data set balanced in terms of 1’s and 0’s. All you need is sufficient number of 1’s for the maximum likelihood to converge. Looking at the distribution of 1’s (10 |
10,032 | Adding weights to logistic regression for imbalanced data | Weighting is a procedure that weights the data to compensate for differences in sample and population (King 2001). For example, in rare events (such as fraud in credit risk, deaths in medical literature) we tend to sample all the 1’s (rare events) and a fraction of 0’s (non events). In such cases we have to weight the ... | Adding weights to logistic regression for imbalanced data | Weighting is a procedure that weights the data to compensate for differences in sample and population (King 2001). For example, in rare events (such as fraud in credit risk, deaths in medical literatu | Adding weights to logistic regression for imbalanced data
Weighting is a procedure that weights the data to compensate for differences in sample and population (King 2001). For example, in rare events (such as fraud in credit risk, deaths in medical literature) we tend to sample all the 1’s (rare events) and a fraction... | Adding weights to logistic regression for imbalanced data
Weighting is a procedure that weights the data to compensate for differences in sample and population (King 2001). For example, in rare events (such as fraud in credit risk, deaths in medical literatu |
10,033 | How to describe or visualize a multiple linear regression model | My favorite way of showing the results of a basic multiple linear regression is to first fit the model to normalized (continuous) variables. That is, z-transform the $X$s by subtracting the mean and dividing by the standard deviation, then fit the model and estimate the parameters. When the variables are transformed in... | How to describe or visualize a multiple linear regression model | My favorite way of showing the results of a basic multiple linear regression is to first fit the model to normalized (continuous) variables. That is, z-transform the $X$s by subtracting the mean and d | How to describe or visualize a multiple linear regression model
My favorite way of showing the results of a basic multiple linear regression is to first fit the model to normalized (continuous) variables. That is, z-transform the $X$s by subtracting the mean and dividing by the standard deviation, then fit the model an... | How to describe or visualize a multiple linear regression model
My favorite way of showing the results of a basic multiple linear regression is to first fit the model to normalized (continuous) variables. That is, z-transform the $X$s by subtracting the mean and d |
10,034 | How to describe or visualize a multiple linear regression model | Since they all have to do with explaining the contributors for cirrhosis, have you tried doing a bubble/circle chart and use color to indicate the different regressors and circle radius to indicate relative impact upon cirrhosis?
I'm referring here to a Google chart type that looks like this:
And on an unrelated note,... | How to describe or visualize a multiple linear regression model | Since they all have to do with explaining the contributors for cirrhosis, have you tried doing a bubble/circle chart and use color to indicate the different regressors and circle radius to indicate re | How to describe or visualize a multiple linear regression model
Since they all have to do with explaining the contributors for cirrhosis, have you tried doing a bubble/circle chart and use color to indicate the different regressors and circle radius to indicate relative impact upon cirrhosis?
I'm referring here to a G... | How to describe or visualize a multiple linear regression model
Since they all have to do with explaining the contributors for cirrhosis, have you tried doing a bubble/circle chart and use color to indicate the different regressors and circle radius to indicate re |
10,035 | How to describe or visualize a multiple linear regression model | @gregory_britten 's answer was exactly what I was looking for. I figured I would share my implementation in python using pandas, matplotlib, and statsmodels.
X_norm = X4.copy() # This is a pd.Dataframe of the independent variables
X_norm = (X_norm - X_norm.mean()) / X_norm.std()
res_norm = sm.OLS(y_log, sm.add_constan... | How to describe or visualize a multiple linear regression model | @gregory_britten 's answer was exactly what I was looking for. I figured I would share my implementation in python using pandas, matplotlib, and statsmodels.
X_norm = X4.copy() # This is a pd.Datafra | How to describe or visualize a multiple linear regression model
@gregory_britten 's answer was exactly what I was looking for. I figured I would share my implementation in python using pandas, matplotlib, and statsmodels.
X_norm = X4.copy() # This is a pd.Dataframe of the independent variables
X_norm = (X_norm - X_nor... | How to describe or visualize a multiple linear regression model
@gregory_britten 's answer was exactly what I was looking for. I figured I would share my implementation in python using pandas, matplotlib, and statsmodels.
X_norm = X4.copy() # This is a pd.Datafra |
10,036 | How to describe or visualize a multiple linear regression model | The visualization you show in 3 (scatter diagram of actual value against predicted value) is a good one. It can be used for any regressor. In this case, the example you show helps confirm the assumption of linearity, since the points are scattered above and below the line throughout the range.
Another assumption you ha... | How to describe or visualize a multiple linear regression model | The visualization you show in 3 (scatter diagram of actual value against predicted value) is a good one. It can be used for any regressor. In this case, the example you show helps confirm the assumpti | How to describe or visualize a multiple linear regression model
The visualization you show in 3 (scatter diagram of actual value against predicted value) is a good one. It can be used for any regressor. In this case, the example you show helps confirm the assumption of linearity, since the points are scattered above an... | How to describe or visualize a multiple linear regression model
The visualization you show in 3 (scatter diagram of actual value against predicted value) is a good one. It can be used for any regressor. In this case, the example you show helps confirm the assumpti |
10,037 | Geometric interpretation of penalized linear regression | Sorry for my painting skills, I will try to give you the following intuition.
Let $f(\beta)$ be the objective function (for example, MSE in case of regression). Let's imagine the contour plot of this function in red (of course we paint it in the space of $\beta$, here for simplicity $\beta_1$ and $\beta_2$).
There is a... | Geometric interpretation of penalized linear regression | Sorry for my painting skills, I will try to give you the following intuition.
Let $f(\beta)$ be the objective function (for example, MSE in case of regression). Let's imagine the contour plot of this | Geometric interpretation of penalized linear regression
Sorry for my painting skills, I will try to give you the following intuition.
Let $f(\beta)$ be the objective function (for example, MSE in case of regression). Let's imagine the contour plot of this function in red (of course we paint it in the space of $\beta$, ... | Geometric interpretation of penalized linear regression
Sorry for my painting skills, I will try to give you the following intuition.
Let $f(\beta)$ be the objective function (for example, MSE in case of regression). Let's imagine the contour plot of this |
10,038 | Geometric interpretation of penalized linear regression | The intuition I have is the following:
In the least-squares case, the hat matrix is an orthogonal projection thus idempotent. In the penalized case, the hat matrix is no longer idempotent. Actually, applying it infinitely many times, will shrink the coefficients to the origin. On the other hand, the coefficients still ... | Geometric interpretation of penalized linear regression | The intuition I have is the following:
In the least-squares case, the hat matrix is an orthogonal projection thus idempotent. In the penalized case, the hat matrix is no longer idempotent. Actually, a | Geometric interpretation of penalized linear regression
The intuition I have is the following:
In the least-squares case, the hat matrix is an orthogonal projection thus idempotent. In the penalized case, the hat matrix is no longer idempotent. Actually, applying it infinitely many times, will shrink the coefficients t... | Geometric interpretation of penalized linear regression
The intuition I have is the following:
In the least-squares case, the hat matrix is an orthogonal projection thus idempotent. In the penalized case, the hat matrix is no longer idempotent. Actually, a |
10,039 | Geometric interpretation of penalized linear regression | Second image
The geometric interpretation in the second figure will look as following:
With OLS the observations are projected onto a surface spanned by the regressor variables and this finds the point in the plane that has the shortest distance to the observed vector $(y_1,y_2,y_3)$.
With ridge regression we do not lo... | Geometric interpretation of penalized linear regression | Second image
The geometric interpretation in the second figure will look as following:
With OLS the observations are projected onto a surface spanned by the regressor variables and this finds the poin | Geometric interpretation of penalized linear regression
Second image
The geometric interpretation in the second figure will look as following:
With OLS the observations are projected onto a surface spanned by the regressor variables and this finds the point in the plane that has the shortest distance to the observed ve... | Geometric interpretation of penalized linear regression
Second image
The geometric interpretation in the second figure will look as following:
With OLS the observations are projected onto a surface spanned by the regressor variables and this finds the poin |
10,040 | What is the difference between Kalman filter and moving average? | A random walk + noise model can be shown to be equivalent to a EWMA (exponentially weighted moving average). The kalman gain ends up being the same as the EWMA weighting.
This is shown to some details in Time Series Analysis by State Space, if you Google Kalman Filter and EWMA you will find a number of resources that d... | What is the difference between Kalman filter and moving average? | A random walk + noise model can be shown to be equivalent to a EWMA (exponentially weighted moving average). The kalman gain ends up being the same as the EWMA weighting.
This is shown to some details | What is the difference between Kalman filter and moving average?
A random walk + noise model can be shown to be equivalent to a EWMA (exponentially weighted moving average). The kalman gain ends up being the same as the EWMA weighting.
This is shown to some details in Time Series Analysis by State Space, if you Google ... | What is the difference between Kalman filter and moving average?
A random walk + noise model can be shown to be equivalent to a EWMA (exponentially weighted moving average). The kalman gain ends up being the same as the EWMA weighting.
This is shown to some details |
10,041 | What is the difference between Kalman filter and moving average? | To Start: The equivalence of Kalman filter with EWMA is only for the case of a "random walk plus noise" and it is covered in the book, Forecast Structural Time Series Model and Kalman Filter by Andrew Harvey. The equivalence of EWMA with Kalman filter for random walk with noise is covered on page 175 of the text. There... | What is the difference between Kalman filter and moving average? | To Start: The equivalence of Kalman filter with EWMA is only for the case of a "random walk plus noise" and it is covered in the book, Forecast Structural Time Series Model and Kalman Filter by Andrew | What is the difference between Kalman filter and moving average?
To Start: The equivalence of Kalman filter with EWMA is only for the case of a "random walk plus noise" and it is covered in the book, Forecast Structural Time Series Model and Kalman Filter by Andrew Harvey. The equivalence of EWMA with Kalman filter for... | What is the difference between Kalman filter and moving average?
To Start: The equivalence of Kalman filter with EWMA is only for the case of a "random walk plus noise" and it is covered in the book, Forecast Structural Time Series Model and Kalman Filter by Andrew |
10,042 | What's the Bayesian equivalent of a general goodness of fit test? | I would suggest the book Bayesian Data Analysis as a great source for answering this question (in particular chapter 6) and everything I am about to say. But one of the usual ways that Bayesians attack this problem is by using Posterior Predictive P-values (PPPs). Before I jump into how PPPs would solve this problem le... | What's the Bayesian equivalent of a general goodness of fit test? | I would suggest the book Bayesian Data Analysis as a great source for answering this question (in particular chapter 6) and everything I am about to say. But one of the usual ways that Bayesians attac | What's the Bayesian equivalent of a general goodness of fit test?
I would suggest the book Bayesian Data Analysis as a great source for answering this question (in particular chapter 6) and everything I am about to say. But one of the usual ways that Bayesians attack this problem is by using Posterior Predictive P-valu... | What's the Bayesian equivalent of a general goodness of fit test?
I would suggest the book Bayesian Data Analysis as a great source for answering this question (in particular chapter 6) and everything I am about to say. But one of the usual ways that Bayesians attac |
10,043 | What's the Bayesian equivalent of a general goodness of fit test? | One relatively simple possibility: Smooth tests of goodness of fit e.g.[1] - which frame the alternative in terms of smooth deviations from the null, built by orthogonal polynomials (with respect to the null density as weight-function) would be relatively straightforward to carry over to a Bayesian framework, since the... | What's the Bayesian equivalent of a general goodness of fit test? | One relatively simple possibility: Smooth tests of goodness of fit e.g.[1] - which frame the alternative in terms of smooth deviations from the null, built by orthogonal polynomials (with respect to t | What's the Bayesian equivalent of a general goodness of fit test?
One relatively simple possibility: Smooth tests of goodness of fit e.g.[1] - which frame the alternative in terms of smooth deviations from the null, built by orthogonal polynomials (with respect to the null density as weight-function) would be relativel... | What's the Bayesian equivalent of a general goodness of fit test?
One relatively simple possibility: Smooth tests of goodness of fit e.g.[1] - which frame the alternative in terms of smooth deviations from the null, built by orthogonal polynomials (with respect to t |
10,044 | Generating visually appealing density heat maps in R | There are two things that will impact the smoothness of the plot, the bandwidth used for your kernel density estimate and the breaks you assign colors to in the plot.
In my experience, for exploratory analysis I just adjust the bandwidth until I get a useful plot. Demonstration below.
library(spatstat)
set.seed(3)
X <... | Generating visually appealing density heat maps in R | There are two things that will impact the smoothness of the plot, the bandwidth used for your kernel density estimate and the breaks you assign colors to in the plot.
In my experience, for explorator | Generating visually appealing density heat maps in R
There are two things that will impact the smoothness of the plot, the bandwidth used for your kernel density estimate and the breaks you assign colors to in the plot.
In my experience, for exploratory analysis I just adjust the bandwidth until I get a useful plot. D... | Generating visually appealing density heat maps in R
There are two things that will impact the smoothness of the plot, the bandwidth used for your kernel density estimate and the breaks you assign colors to in the plot.
In my experience, for explorator |
10,045 | Generating visually appealing density heat maps in R | You may benefit from the interp function from the akima package. This will let you interpolate your matrix to another resolution if need be. To make something like your linked example, you would need to interpolate to a pretty fine grid (perhaps with the arguments xo and yo being ~ 1000 in length). This will give you a... | Generating visually appealing density heat maps in R | You may benefit from the interp function from the akima package. This will let you interpolate your matrix to another resolution if need be. To make something like your linked example, you would need | Generating visually appealing density heat maps in R
You may benefit from the interp function from the akima package. This will let you interpolate your matrix to another resolution if need be. To make something like your linked example, you would need to interpolate to a pretty fine grid (perhaps with the arguments xo... | Generating visually appealing density heat maps in R
You may benefit from the interp function from the akima package. This will let you interpolate your matrix to another resolution if need be. To make something like your linked example, you would need |
10,046 | Generating visually appealing density heat maps in R | You may want to look into ggplot2. It seems like the package you've tried doesn't have a great color schemes or "flow" -- take a look at RColorBrewer. There is a blog where it implemented these package with a simple example.
I'm not sure if you are trying to plot geographical data as shown in your linked example, but ... | Generating visually appealing density heat maps in R | You may want to look into ggplot2. It seems like the package you've tried doesn't have a great color schemes or "flow" -- take a look at RColorBrewer. There is a blog where it implemented these packag | Generating visually appealing density heat maps in R
You may want to look into ggplot2. It seems like the package you've tried doesn't have a great color schemes or "flow" -- take a look at RColorBrewer. There is a blog where it implemented these package with a simple example.
I'm not sure if you are trying to plot ge... | Generating visually appealing density heat maps in R
You may want to look into ggplot2. It seems like the package you've tried doesn't have a great color schemes or "flow" -- take a look at RColorBrewer. There is a blog where it implemented these packag |
10,047 | Generating visually appealing density heat maps in R | Have you tried cranking up the resolution in density? Try argument dimyx=c(512, 512) or higher. | Generating visually appealing density heat maps in R | Have you tried cranking up the resolution in density? Try argument dimyx=c(512, 512) or higher. | Generating visually appealing density heat maps in R
Have you tried cranking up the resolution in density? Try argument dimyx=c(512, 512) or higher. | Generating visually appealing density heat maps in R
Have you tried cranking up the resolution in density? Try argument dimyx=c(512, 512) or higher. |
10,048 | Basic Simpson's paradox | I think A and E aren't a good combination, because A says you should pick Mercy and E says you should pick Hope.
A and D have the virtue of advocating the same choice. But, lets examine the line of reasoning in D in further detail, since that seems to be the confusion. The probability of success for the surgeries f... | Basic Simpson's paradox | I think A and E aren't a good combination, because A says you should pick Mercy and E says you should pick Hope.
A and D have the virtue of advocating the same choice. But, lets examine the line of | Basic Simpson's paradox
I think A and E aren't a good combination, because A says you should pick Mercy and E says you should pick Hope.
A and D have the virtue of advocating the same choice. But, lets examine the line of reasoning in D in further detail, since that seems to be the confusion. The probability of suc... | Basic Simpson's paradox
I think A and E aren't a good combination, because A says you should pick Mercy and E says you should pick Hope.
A and D have the virtue of advocating the same choice. But, lets examine the line of |
10,049 | Basic Simpson's paradox | None of the answers are entirely baseless. But they ALL assume significant external knowledge and can't be taken to be correct strictly on the basis of the statistics.
A, B, D, and E all require assumptions about the factors the cause patients to choose one hospital over another; the process by which doctors and patie... | Basic Simpson's paradox | None of the answers are entirely baseless. But they ALL assume significant external knowledge and can't be taken to be correct strictly on the basis of the statistics.
A, B, D, and E all require assu | Basic Simpson's paradox
None of the answers are entirely baseless. But they ALL assume significant external knowledge and can't be taken to be correct strictly on the basis of the statistics.
A, B, D, and E all require assumptions about the factors the cause patients to choose one hospital over another; the process by... | Basic Simpson's paradox
None of the answers are entirely baseless. But they ALL assume significant external knowledge and can't be taken to be correct strictly on the basis of the statistics.
A, B, D, and E all require assu |
10,050 | Basic Simpson's paradox | @gung gave a very thorough answer, but there is one more reason why D is a correct answer to the question: Better hospitals do more of the difficult operations because they are better. That is, if a person comes into Hope Hospital for operation E (the hardest) they may send him/her to Mercy because they at Hope don't k... | Basic Simpson's paradox | @gung gave a very thorough answer, but there is one more reason why D is a correct answer to the question: Better hospitals do more of the difficult operations because they are better. That is, if a p | Basic Simpson's paradox
@gung gave a very thorough answer, but there is one more reason why D is a correct answer to the question: Better hospitals do more of the difficult operations because they are better. That is, if a person comes into Hope Hospital for operation E (the hardest) they may send him/her to Mercy beca... | Basic Simpson's paradox
@gung gave a very thorough answer, but there is one more reason why D is a correct answer to the question: Better hospitals do more of the difficult operations because they are better. That is, if a p |
10,051 | Regression vs. ANOVA discrepancy (aov vs lm in R) | The summary function calls different methods depending on the class of the object. The difference isn't in the aov vs lm, but in the information presented about the models. For example, if you used anova(mod1) and anova(mod2) instead, you should get the same results.
As @Glen says, the key is whether the tests repo... | Regression vs. ANOVA discrepancy (aov vs lm in R) | The summary function calls different methods depending on the class of the object. The difference isn't in the aov vs lm, but in the information presented about the models. For example, if you used | Regression vs. ANOVA discrepancy (aov vs lm in R)
The summary function calls different methods depending on the class of the object. The difference isn't in the aov vs lm, but in the information presented about the models. For example, if you used anova(mod1) and anova(mod2) instead, you should get the same results. ... | Regression vs. ANOVA discrepancy (aov vs lm in R)
The summary function calls different methods depending on the class of the object. The difference isn't in the aov vs lm, but in the information presented about the models. For example, if you used |
10,052 | Regression vs. ANOVA discrepancy (aov vs lm in R) | The results from the aov output are giving you probabilities based on Type 1 sum of squares. This is why the interaction result is the same and the main effects differ.
If you use probabilities based on Type 3 sum of squares then they will match the linear regression results.
library(car)
Anova(aov(score~group*moderat... | Regression vs. ANOVA discrepancy (aov vs lm in R) | The results from the aov output are giving you probabilities based on Type 1 sum of squares. This is why the interaction result is the same and the main effects differ.
If you use probabilities based | Regression vs. ANOVA discrepancy (aov vs lm in R)
The results from the aov output are giving you probabilities based on Type 1 sum of squares. This is why the interaction result is the same and the main effects differ.
If you use probabilities based on Type 3 sum of squares then they will match the linear regression r... | Regression vs. ANOVA discrepancy (aov vs lm in R)
The results from the aov output are giving you probabilities based on Type 1 sum of squares. This is why the interaction result is the same and the main effects differ.
If you use probabilities based |
10,053 | Regression vs. ANOVA discrepancy (aov vs lm in R) | The main difference between linear regression and ANOVA is, in ANOVA the predictor variables are discrete (that is they have different levels). Whereas in linear regression, the predictor variables are continuous. | Regression vs. ANOVA discrepancy (aov vs lm in R) | The main difference between linear regression and ANOVA is, in ANOVA the predictor variables are discrete (that is they have different levels). Whereas in linear regression, the predictor variables ar | Regression vs. ANOVA discrepancy (aov vs lm in R)
The main difference between linear regression and ANOVA is, in ANOVA the predictor variables are discrete (that is they have different levels). Whereas in linear regression, the predictor variables are continuous. | Regression vs. ANOVA discrepancy (aov vs lm in R)
The main difference between linear regression and ANOVA is, in ANOVA the predictor variables are discrete (that is they have different levels). Whereas in linear regression, the predictor variables ar |
10,054 | Average ROC for repeated 10-fold cross validation with probability estimates | From your description it seems to make perfect sense: not only you may calculate the mean ROC curve, but also the variance around it to build confidence intervals. It should give you the idea of how stable your model is.
For example, like this:
Here I put individual ROC curves as well as the mean curve and the confide... | Average ROC for repeated 10-fold cross validation with probability estimates | From your description it seems to make perfect sense: not only you may calculate the mean ROC curve, but also the variance around it to build confidence intervals. It should give you the idea of how s | Average ROC for repeated 10-fold cross validation with probability estimates
From your description it seems to make perfect sense: not only you may calculate the mean ROC curve, but also the variance around it to build confidence intervals. It should give you the idea of how stable your model is.
For example, like this... | Average ROC for repeated 10-fold cross validation with probability estimates
From your description it seems to make perfect sense: not only you may calculate the mean ROC curve, but also the variance around it to build confidence intervals. It should give you the idea of how s |
10,055 | Average ROC for repeated 10-fold cross validation with probability estimates | It is not correct to average probabilities because that would not represent the predictions you are trying to validate and involves contamination across validation samples.
Note that 100 repeats of 10-fold cross-validation may be required to achieve adequate precision. Or use the Efron-Gong optimism bootstrap which re... | Average ROC for repeated 10-fold cross validation with probability estimates | It is not correct to average probabilities because that would not represent the predictions you are trying to validate and involves contamination across validation samples.
Note that 100 repeats of 10 | Average ROC for repeated 10-fold cross validation with probability estimates
It is not correct to average probabilities because that would not represent the predictions you are trying to validate and involves contamination across validation samples.
Note that 100 repeats of 10-fold cross-validation may be required to a... | Average ROC for repeated 10-fold cross validation with probability estimates
It is not correct to average probabilities because that would not represent the predictions you are trying to validate and involves contamination across validation samples.
Note that 100 repeats of 10 |
10,056 | Central limit theorem and the law of large numbers | This figure shows the distributions of the means of $n=1$ (blue), $10$ (red), and $100$ (gold) independent and identically distributed (iid) normal distributions (of unit variance and mean $\mu$):
As $n$ increases, the distribution of the mean becomes more "focused" on $\mu$. (The sense of "focusing" is easily quanti... | Central limit theorem and the law of large numbers | This figure shows the distributions of the means of $n=1$ (blue), $10$ (red), and $100$ (gold) independent and identically distributed (iid) normal distributions (of unit variance and mean $\mu$):
As | Central limit theorem and the law of large numbers
This figure shows the distributions of the means of $n=1$ (blue), $10$ (red), and $100$ (gold) independent and identically distributed (iid) normal distributions (of unit variance and mean $\mu$):
As $n$ increases, the distribution of the mean becomes more "focused" o... | Central limit theorem and the law of large numbers
This figure shows the distributions of the means of $n=1$ (blue), $10$ (red), and $100$ (gold) independent and identically distributed (iid) normal distributions (of unit variance and mean $\mu$):
As |
10,057 | Difference between missing data and sparse data in machine learning algorithms | For the ease of understanding, I'll describe this using an example.
Let's say that you are collecting data from a device which has 12 sensors. And you have collected data for 10 days.
The data you have collected is as follows:
This is called sparse data because most of the sensor outputs are zero. Which means those se... | Difference between missing data and sparse data in machine learning algorithms | For the ease of understanding, I'll describe this using an example.
Let's say that you are collecting data from a device which has 12 sensors. And you have collected data for 10 days.
The data you hav | Difference between missing data and sparse data in machine learning algorithms
For the ease of understanding, I'll describe this using an example.
Let's say that you are collecting data from a device which has 12 sensors. And you have collected data for 10 days.
The data you have collected is as follows:
This is calle... | Difference between missing data and sparse data in machine learning algorithms
For the ease of understanding, I'll describe this using an example.
Let's say that you are collecting data from a device which has 12 sensors. And you have collected data for 10 days.
The data you hav |
10,058 | What are the multidimensional versions of median [duplicate] | The Geometric Median is a generalization of the median to higher dimensions.
One of the properties of the median is that it is a point with minimal distance to all other points in the set, and GM generalizes this notion (using Euclidean/L2 distance).
Regarding robustness, the Wikipedia article mentions that: "The geome... | What are the multidimensional versions of median [duplicate] | The Geometric Median is a generalization of the median to higher dimensions.
One of the properties of the median is that it is a point with minimal distance to all other points in the set, and GM gene | What are the multidimensional versions of median [duplicate]
The Geometric Median is a generalization of the median to higher dimensions.
One of the properties of the median is that it is a point with minimal distance to all other points in the set, and GM generalizes this notion (using Euclidean/L2 distance).
Regardin... | What are the multidimensional versions of median [duplicate]
The Geometric Median is a generalization of the median to higher dimensions.
One of the properties of the median is that it is a point with minimal distance to all other points in the set, and GM gene |
10,059 | Statistics/Probability Videos for Beginners | I think a number of the suggestions put forward on the mathematical statistics video question probably fall in the stats 101 category:
http://www.khanacademy.org/#Statistics: series of short videos on introductory statistics
http://www.khanacademy.org/#Probability: series of short videos on introductory probability
Ma... | Statistics/Probability Videos for Beginners | I think a number of the suggestions put forward on the mathematical statistics video question probably fall in the stats 101 category:
http://www.khanacademy.org/#Statistics: series of short videos o | Statistics/Probability Videos for Beginners
I think a number of the suggestions put forward on the mathematical statistics video question probably fall in the stats 101 category:
http://www.khanacademy.org/#Statistics: series of short videos on introductory statistics
http://www.khanacademy.org/#Probability: series of... | Statistics/Probability Videos for Beginners
I think a number of the suggestions put forward on the mathematical statistics video question probably fall in the stats 101 category:
http://www.khanacademy.org/#Statistics: series of short videos o |
10,060 | Statistics/Probability Videos for Beginners | I would suggest Statistics 401 (from Iowa State University).
This is the link for Lesson 1. There are 64 lectures in total. (Here is the last one). Unfortunately, I haven't found the links gathered together in one page. I think also that lessons 21,26 and 39 are missing. However, I don't remember having any problem in... | Statistics/Probability Videos for Beginners | I would suggest Statistics 401 (from Iowa State University).
This is the link for Lesson 1. There are 64 lectures in total. (Here is the last one). Unfortunately, I haven't found the links gathered t | Statistics/Probability Videos for Beginners
I would suggest Statistics 401 (from Iowa State University).
This is the link for Lesson 1. There are 64 lectures in total. (Here is the last one). Unfortunately, I haven't found the links gathered together in one page. I think also that lessons 21,26 and 39 are missing. How... | Statistics/Probability Videos for Beginners
I would suggest Statistics 401 (from Iowa State University).
This is the link for Lesson 1. There are 64 lectures in total. (Here is the last one). Unfortunately, I haven't found the links gathered t |
10,061 | Statistics/Probability Videos for Beginners | GD047 linked to a UC Berkeley video on statistics, and I had another one. It's similar to MIT's Open Courseware (maybe a little lower quality). This one is for the Intro Statistics and Probability Class. It unfortunately only has 9 episodes, but it's free :).
UC Berkeley STATS 20 Webcast | Statistics/Probability Videos for Beginners | GD047 linked to a UC Berkeley video on statistics, and I had another one. It's similar to MIT's Open Courseware (maybe a little lower quality). This one is for the Intro Statistics and Probability Cla | Statistics/Probability Videos for Beginners
GD047 linked to a UC Berkeley video on statistics, and I had another one. It's similar to MIT's Open Courseware (maybe a little lower quality). This one is for the Intro Statistics and Probability Class. It unfortunately only has 9 episodes, but it's free :).
UC Berkeley STAT... | Statistics/Probability Videos for Beginners
GD047 linked to a UC Berkeley video on statistics, and I had another one. It's similar to MIT's Open Courseware (maybe a little lower quality). This one is for the Intro Statistics and Probability Cla |
10,062 | Statistics/Probability Videos for Beginners | Harvard's Stat 110 Probability taught by Joseph Blitzstein is available on iTunesU or Academic Earth. | Statistics/Probability Videos for Beginners | Harvard's Stat 110 Probability taught by Joseph Blitzstein is available on iTunesU or Academic Earth. | Statistics/Probability Videos for Beginners
Harvard's Stat 110 Probability taught by Joseph Blitzstein is available on iTunesU or Academic Earth. | Statistics/Probability Videos for Beginners
Harvard's Stat 110 Probability taught by Joseph Blitzstein is available on iTunesU or Academic Earth. |
10,063 | Statistics/Probability Videos for Beginners | You should definitely consider the Stats courses on Udacity.
ST101: http://www.udacity.com/overview/Course/st101/CourseRev/1
There is also a ST095 course on Udacity, which I would have linked to but the link was blocked in this answer. Replace the 101 in the above link with 095.
EdX, which is a consortium between Harv... | Statistics/Probability Videos for Beginners | You should definitely consider the Stats courses on Udacity.
ST101: http://www.udacity.com/overview/Course/st101/CourseRev/1
There is also a ST095 course on Udacity, which I would have linked to but | Statistics/Probability Videos for Beginners
You should definitely consider the Stats courses on Udacity.
ST101: http://www.udacity.com/overview/Course/st101/CourseRev/1
There is also a ST095 course on Udacity, which I would have linked to but the link was blocked in this answer. Replace the 101 in the above link with ... | Statistics/Probability Videos for Beginners
You should definitely consider the Stats courses on Udacity.
ST101: http://www.udacity.com/overview/Course/st101/CourseRev/1
There is also a ST095 course on Udacity, which I would have linked to but |
10,064 | Statistics/Probability Videos for Beginners | I've studied from John E. Freund's Mathematical Statistics with Applications (7th Edition) at Freiburg University. It is indeed a good book, but you would also need a solutions manual to it because the excercises at the end of each chapter are not trivial. | Statistics/Probability Videos for Beginners | I've studied from John E. Freund's Mathematical Statistics with Applications (7th Edition) at Freiburg University. It is indeed a good book, but you would also need a solutions manual to it because th | Statistics/Probability Videos for Beginners
I've studied from John E. Freund's Mathematical Statistics with Applications (7th Edition) at Freiburg University. It is indeed a good book, but you would also need a solutions manual to it because the excercises at the end of each chapter are not trivial. | Statistics/Probability Videos for Beginners
I've studied from John E. Freund's Mathematical Statistics with Applications (7th Edition) at Freiburg University. It is indeed a good book, but you would also need a solutions manual to it because th |
10,065 | When are genetic algorithms a good choice for optimization? | Genetic algorithms (GA) are a family of heuristics which are empirically good at providing a decent answer in many cases, although they are rarely the best option for a given domain.
You mention derivative-based algorithms, but even in the absence of derivatives there are plenty of derivative-free optimization algorith... | When are genetic algorithms a good choice for optimization? | Genetic algorithms (GA) are a family of heuristics which are empirically good at providing a decent answer in many cases, although they are rarely the best option for a given domain.
You mention deriv | When are genetic algorithms a good choice for optimization?
Genetic algorithms (GA) are a family of heuristics which are empirically good at providing a decent answer in many cases, although they are rarely the best option for a given domain.
You mention derivative-based algorithms, but even in the absence of derivativ... | When are genetic algorithms a good choice for optimization?
Genetic algorithms (GA) are a family of heuristics which are empirically good at providing a decent answer in many cases, although they are rarely the best option for a given domain.
You mention deriv |
10,066 | When are genetic algorithms a good choice for optimization? | Genetic methods are well suited for multicriteria optimization when gradient descent is dedicated to monocriteria optimization. Gradient descent allow to find minimum of functions when derivatives exists and there is only one optimum solution (if we except local minimas). A genetics algorithm can be used in multicriter... | When are genetic algorithms a good choice for optimization? | Genetic methods are well suited for multicriteria optimization when gradient descent is dedicated to monocriteria optimization. Gradient descent allow to find minimum of functions when derivatives exi | When are genetic algorithms a good choice for optimization?
Genetic methods are well suited for multicriteria optimization when gradient descent is dedicated to monocriteria optimization. Gradient descent allow to find minimum of functions when derivatives exists and there is only one optimum solution (if we except loc... | When are genetic algorithms a good choice for optimization?
Genetic methods are well suited for multicriteria optimization when gradient descent is dedicated to monocriteria optimization. Gradient descent allow to find minimum of functions when derivatives exi |
10,067 | When are genetic algorithms a good choice for optimization? | Best in which sense ?
In my experience, GAs are one of the most pragmatic optimizers.
While many more precise algorithms require time and effort to formalize real problems in the mathematical world, GAs can handle any cost function with complex rules and constraints (GAs are related by an execution approach afterall a... | When are genetic algorithms a good choice for optimization? | Best in which sense ?
In my experience, GAs are one of the most pragmatic optimizers.
While many more precise algorithms require time and effort to formalize real problems in the mathematical world, | When are genetic algorithms a good choice for optimization?
Best in which sense ?
In my experience, GAs are one of the most pragmatic optimizers.
While many more precise algorithms require time and effort to formalize real problems in the mathematical world, GAs can handle any cost function with complex rules and cons... | When are genetic algorithms a good choice for optimization?
Best in which sense ?
In my experience, GAs are one of the most pragmatic optimizers.
While many more precise algorithms require time and effort to formalize real problems in the mathematical world, |
10,068 | When are genetic algorithms a good choice for optimization? | Genetic algorithms are best when many processors can be used in parallel. and when the object function has a high modality (many local optima). Also, for multi-objective optimization, there are multi-objective genetic algorithms, MOGA.
However, I think Genetic algorithms are overrated. A lot of the popularity probably... | When are genetic algorithms a good choice for optimization? | Genetic algorithms are best when many processors can be used in parallel. and when the object function has a high modality (many local optima). Also, for multi-objective optimization, there are multi- | When are genetic algorithms a good choice for optimization?
Genetic algorithms are best when many processors can be used in parallel. and when the object function has a high modality (many local optima). Also, for multi-objective optimization, there are multi-objective genetic algorithms, MOGA.
However, I think Geneti... | When are genetic algorithms a good choice for optimization?
Genetic algorithms are best when many processors can be used in parallel. and when the object function has a high modality (many local optima). Also, for multi-objective optimization, there are multi- |
10,069 | History of uninformative prior theory | What you seem to be missing is the early history. You can check the paper by Fienberg (2006) When Did Bayesian Inference Become "Bayesian"?. First, he notices that Thomas Bayes was the first one who suggested using a uniform prior:
In current statistical language, Bayes' paper introduces a uniform
prior distribution... | History of uninformative prior theory | What you seem to be missing is the early history. You can check the paper by Fienberg (2006) When Did Bayesian Inference Become "Bayesian"?. First, he notices that Thomas Bayes was the first one who s | History of uninformative prior theory
What you seem to be missing is the early history. You can check the paper by Fienberg (2006) When Did Bayesian Inference Become "Bayesian"?. First, he notices that Thomas Bayes was the first one who suggested using a uniform prior:
In current statistical language, Bayes' paper int... | History of uninformative prior theory
What you seem to be missing is the early history. You can check the paper by Fienberg (2006) When Did Bayesian Inference Become "Bayesian"?. First, he notices that Thomas Bayes was the first one who s |
10,070 | History of uninformative prior theory | A few comments about flaws of noninformative priors (uninformative priors) are probably a good idea since the investigation of such flaws helped development of the concept of noninformative prior in history.
You may want to add some comments about the drawbacks/flaws of adopting noninformative priors. Among many criti... | History of uninformative prior theory | A few comments about flaws of noninformative priors (uninformative priors) are probably a good idea since the investigation of such flaws helped development of the concept of noninformative prior in h | History of uninformative prior theory
A few comments about flaws of noninformative priors (uninformative priors) are probably a good idea since the investigation of such flaws helped development of the concept of noninformative prior in history.
You may want to add some comments about the drawbacks/flaws of adopting n... | History of uninformative prior theory
A few comments about flaws of noninformative priors (uninformative priors) are probably a good idea since the investigation of such flaws helped development of the concept of noninformative prior in h |
10,071 | History of uninformative prior theory | I would have posted in the comments, but I guess I do not have the reputation yet. The only missing thing, not in the comments already marked, is a special case of noninformative priors whose origins that I have tried to hunt down and have not found. It may precede Jeffreys paper.
For the normal distribution, I have ... | History of uninformative prior theory | I would have posted in the comments, but I guess I do not have the reputation yet. The only missing thing, not in the comments already marked, is a special case of noninformative priors whose origins | History of uninformative prior theory
I would have posted in the comments, but I guess I do not have the reputation yet. The only missing thing, not in the comments already marked, is a special case of noninformative priors whose origins that I have tried to hunt down and have not found. It may precede Jeffreys paper... | History of uninformative prior theory
I would have posted in the comments, but I guess I do not have the reputation yet. The only missing thing, not in the comments already marked, is a special case of noninformative priors whose origins |
10,072 | Support vector machines and regression | Basically they generalize in the same way. The kernel based approach to regression is to transform the feature, call it $\mathbf{x}$ to some vector space, then perform a linear regression in that vector space. To avoid the 'curse of dimensionality', the linear regression in the transformed space is somewhat different t... | Support vector machines and regression | Basically they generalize in the same way. The kernel based approach to regression is to transform the feature, call it $\mathbf{x}$ to some vector space, then perform a linear regression in that vect | Support vector machines and regression
Basically they generalize in the same way. The kernel based approach to regression is to transform the feature, call it $\mathbf{x}$ to some vector space, then perform a linear regression in that vector space. To avoid the 'curse of dimensionality', the linear regression in the tr... | Support vector machines and regression
Basically they generalize in the same way. The kernel based approach to regression is to transform the feature, call it $\mathbf{x}$ to some vector space, then perform a linear regression in that vect |
10,073 | Support vector machines and regression | For an overview of SVM: How does a Support Vector Machine (SVM) work?
Regarding support vector regression (SVR), I find these slides from http://cs.adelaide.edu.au/~chhshen/teaching/ML_SVR.pdf (mirror) very clear:
The Matlab documentation also has a decent explanation and additionally goes over the optimizati... | Support vector machines and regression | For an overview of SVM: How does a Support Vector Machine (SVM) work?
Regarding support vector regression (SVR), I find these slides from http://cs.adelaide.edu.au/~chhshen/teaching/ML_SVR.pdf (mirro | Support vector machines and regression
For an overview of SVM: How does a Support Vector Machine (SVM) work?
Regarding support vector regression (SVR), I find these slides from http://cs.adelaide.edu.au/~chhshen/teaching/ML_SVR.pdf (mirror) very clear:
The Matlab documentation also has a decent explanation an... | Support vector machines and regression
For an overview of SVM: How does a Support Vector Machine (SVM) work?
Regarding support vector regression (SVR), I find these slides from http://cs.adelaide.edu.au/~chhshen/teaching/ML_SVR.pdf (mirro |
10,074 | What's the difference between binomial regression and logistic regression? | Logistic regression is a binomial regression with the "logistic" link function:
$$g(p)=\log\left(\frac{p}{1-p}\right)=X\beta$$
Although I also think logistic regression is usually applied to binomial proportions rather than binomial counts. | What's the difference between binomial regression and logistic regression? | Logistic regression is a binomial regression with the "logistic" link function:
$$g(p)=\log\left(\frac{p}{1-p}\right)=X\beta$$
Although I also think logistic regression is usually applied to binomial | What's the difference between binomial regression and logistic regression?
Logistic regression is a binomial regression with the "logistic" link function:
$$g(p)=\log\left(\frac{p}{1-p}\right)=X\beta$$
Although I also think logistic regression is usually applied to binomial proportions rather than binomial counts. | What's the difference between binomial regression and logistic regression?
Logistic regression is a binomial regression with the "logistic" link function:
$$g(p)=\log\left(\frac{p}{1-p}\right)=X\beta$$
Although I also think logistic regression is usually applied to binomial |
10,075 | What's the difference between binomial regression and logistic regression? | Binomial regression is any type of GLM using a binomial mean-variance relationship where the variance is given by $\mbox{var}(Y) = \hat{Y}(1-\hat{Y})$. In logistic regression the $\hat{Y} = \mbox{logit}^{-1}(\mathbf{X}\hat{\beta})=1/(1-\exp{(\mathbf{X}\hat{\beta})})$ with the logit function said to be a "link" function... | What's the difference between binomial regression and logistic regression? | Binomial regression is any type of GLM using a binomial mean-variance relationship where the variance is given by $\mbox{var}(Y) = \hat{Y}(1-\hat{Y})$. In logistic regression the $\hat{Y} = \mbox{logi | What's the difference between binomial regression and logistic regression?
Binomial regression is any type of GLM using a binomial mean-variance relationship where the variance is given by $\mbox{var}(Y) = \hat{Y}(1-\hat{Y})$. In logistic regression the $\hat{Y} = \mbox{logit}^{-1}(\mathbf{X}\hat{\beta})=1/(1-\exp{(\ma... | What's the difference between binomial regression and logistic regression?
Binomial regression is any type of GLM using a binomial mean-variance relationship where the variance is given by $\mbox{var}(Y) = \hat{Y}(1-\hat{Y})$. In logistic regression the $\hat{Y} = \mbox{logi |
10,076 | Differences between heavy tail and fat tail distributions | I would say that the usual definition in applied probability theory is that a right heavy tailed distribution is one with infinite moment generating function on $(0, \infty)$, that is, $X$ has right heavy tail if
$$E(e^{tX}) = \infty, \quad t > 0.$$
This is in agreement with Wikipedia, which does mention other used de... | Differences between heavy tail and fat tail distributions | I would say that the usual definition in applied probability theory is that a right heavy tailed distribution is one with infinite moment generating function on $(0, \infty)$, that is, $X$ has right h | Differences between heavy tail and fat tail distributions
I would say that the usual definition in applied probability theory is that a right heavy tailed distribution is one with infinite moment generating function on $(0, \infty)$, that is, $X$ has right heavy tail if
$$E(e^{tX}) = \infty, \quad t > 0.$$
This is in ... | Differences between heavy tail and fat tail distributions
I would say that the usual definition in applied probability theory is that a right heavy tailed distribution is one with infinite moment generating function on $(0, \infty)$, that is, $X$ has right h |
10,077 | Differences between heavy tail and fat tail distributions | NN Taleb, P Cirillo (2019) address this directly in Branching epistemic uncertainty and thickness of tails where they state:
From the point of view of extreme value statistics, both
the Gamma and the Lognormal are heavy-tailed distributions,
meaning that their right tail goes to zero slower than an
exponential function... | Differences between heavy tail and fat tail distributions | NN Taleb, P Cirillo (2019) address this directly in Branching epistemic uncertainty and thickness of tails where they state:
From the point of view of extreme value statistics, both
the Gamma and the | Differences between heavy tail and fat tail distributions
NN Taleb, P Cirillo (2019) address this directly in Branching epistemic uncertainty and thickness of tails where they state:
From the point of view of extreme value statistics, both
the Gamma and the Lognormal are heavy-tailed distributions,
meaning that their r... | Differences between heavy tail and fat tail distributions
NN Taleb, P Cirillo (2019) address this directly in Branching epistemic uncertainty and thickness of tails where they state:
From the point of view of extreme value statistics, both
the Gamma and the |
10,078 | Differences between heavy tail and fat tail distributions | First there can be left tails and right tails, then long tails and short tails. A short-tailed distribution can be thought of a having a finite range, called its support. And a long tail has semi-infinite support in that direction. For right-tail heaviness one does a comparison of either survival functions (RVs) or com... | Differences between heavy tail and fat tail distributions | First there can be left tails and right tails, then long tails and short tails. A short-tailed distribution can be thought of a having a finite range, called its support. And a long tail has semi-infi | Differences between heavy tail and fat tail distributions
First there can be left tails and right tails, then long tails and short tails. A short-tailed distribution can be thought of a having a finite range, called its support. And a long tail has semi-infinite support in that direction. For right-tail heaviness one d... | Differences between heavy tail and fat tail distributions
First there can be left tails and right tails, then long tails and short tails. A short-tailed distribution can be thought of a having a finite range, called its support. And a long tail has semi-infi |
10,079 | STL trend of time series using R | I wouldn't bother with stl() for this - the bandwidth for the lowess smoother used to extract the trend is far, far, to small resulting in the small scale fluctuations you see. I would use an additive model. Here is an example using data and model code from Simon Wood's book on GAMs:
require(mgcv)
require(gamair)
data(... | STL trend of time series using R | I wouldn't bother with stl() for this - the bandwidth for the lowess smoother used to extract the trend is far, far, to small resulting in the small scale fluctuations you see. I would use an additive | STL trend of time series using R
I wouldn't bother with stl() for this - the bandwidth for the lowess smoother used to extract the trend is far, far, to small resulting in the small scale fluctuations you see. I would use an additive model. Here is an example using data and model code from Simon Wood's book on GAMs:
re... | STL trend of time series using R
I wouldn't bother with stl() for this - the bandwidth for the lowess smoother used to extract the trend is far, far, to small resulting in the small scale fluctuations you see. I would use an additive |
10,080 | STL trend of time series using R | Gavin provided a very thorough answer, but for a simpler and faster solution, I recommend setting the stl function t.window parameter to a value that is a multiple of the frequency of the ts data. I would use the inferred periodicity of interest (e.g., a value of 3660 for decadal trends with diurnal resolution data). Y... | STL trend of time series using R | Gavin provided a very thorough answer, but for a simpler and faster solution, I recommend setting the stl function t.window parameter to a value that is a multiple of the frequency of the ts data. I w | STL trend of time series using R
Gavin provided a very thorough answer, but for a simpler and faster solution, I recommend setting the stl function t.window parameter to a value that is a multiple of the frequency of the ts data. I would use the inferred periodicity of interest (e.g., a value of 3660 for decadal trends... | STL trend of time series using R
Gavin provided a very thorough answer, but for a simpler and faster solution, I recommend setting the stl function t.window parameter to a value that is a multiple of the frequency of the ts data. I w |
10,081 | Is Gradient Descent possible for kernelized SVMs (if so, why do people use Quadratic Programming)? | Set $\mathbf w = \phi(\mathbf x)\cdot \mathbf u$ so that $\mathbf w^t \phi(\mathbf x)=\mathbf u^t \cdot \mathbf K$ and $\mathbf w^t\mathbf w = \mathbf u^t\mathbf K\mathbf u$, with $\mathbf K = \phi(\mathbf x)^t\phi(\mathbf x)$, where $\phi(x)$ is a mapping of the original input matrix, $\mathbf x$. This allows one to s... | Is Gradient Descent possible for kernelized SVMs (if so, why do people use Quadratic Programming)? | Set $\mathbf w = \phi(\mathbf x)\cdot \mathbf u$ so that $\mathbf w^t \phi(\mathbf x)=\mathbf u^t \cdot \mathbf K$ and $\mathbf w^t\mathbf w = \mathbf u^t\mathbf K\mathbf u$, with $\mathbf K = \phi(\m | Is Gradient Descent possible for kernelized SVMs (if so, why do people use Quadratic Programming)?
Set $\mathbf w = \phi(\mathbf x)\cdot \mathbf u$ so that $\mathbf w^t \phi(\mathbf x)=\mathbf u^t \cdot \mathbf K$ and $\mathbf w^t\mathbf w = \mathbf u^t\mathbf K\mathbf u$, with $\mathbf K = \phi(\mathbf x)^t\phi(\mathb... | Is Gradient Descent possible for kernelized SVMs (if so, why do people use Quadratic Programming)?
Set $\mathbf w = \phi(\mathbf x)\cdot \mathbf u$ so that $\mathbf w^t \phi(\mathbf x)=\mathbf u^t \cdot \mathbf K$ and $\mathbf w^t\mathbf w = \mathbf u^t\mathbf K\mathbf u$, with $\mathbf K = \phi(\m |
10,082 | Is Gradient Descent possible for kernelized SVMs (if so, why do people use Quadratic Programming)? | If we apply a transformation $\phi$ to all input weight vectors ($\mathbf{x}^{(i)}$), we get the following cost function:
$J(\mathbf{w}, b) = C {\displaystyle \sum\limits_{i=1}^{m} max\left(0, 1 - y^{(i)} (\mathbf{w}^t \cdot \phi(\mathbf{x}^{(i)}) + b)\right)} \quad + \quad \dfrac{1}{2} \mathbf{w}^t \cdot \mathbf{w}$
T... | Is Gradient Descent possible for kernelized SVMs (if so, why do people use Quadratic Programming)? | If we apply a transformation $\phi$ to all input weight vectors ($\mathbf{x}^{(i)}$), we get the following cost function:
$J(\mathbf{w}, b) = C {\displaystyle \sum\limits_{i=1}^{m} max\left(0, 1 - y^{ | Is Gradient Descent possible for kernelized SVMs (if so, why do people use Quadratic Programming)?
If we apply a transformation $\phi$ to all input weight vectors ($\mathbf{x}^{(i)}$), we get the following cost function:
$J(\mathbf{w}, b) = C {\displaystyle \sum\limits_{i=1}^{m} max\left(0, 1 - y^{(i)} (\mathbf{w}^t \c... | Is Gradient Descent possible for kernelized SVMs (if so, why do people use Quadratic Programming)?
If we apply a transformation $\phi$ to all input weight vectors ($\mathbf{x}^{(i)}$), we get the following cost function:
$J(\mathbf{w}, b) = C {\displaystyle \sum\limits_{i=1}^{m} max\left(0, 1 - y^{ |
10,083 | Is Gradient Descent possible for kernelized SVMs (if so, why do people use Quadratic Programming)? | I might be wrong, but I don't see how we can replace the dot products with kernels without turning it into the dual problem.
The kernels map the input implicitly to some feature space where $x$ becomes $\phi(x)$, the loss function then becomes
$J(\mathbf{w}, b) = C {\displaystyle \sum\limits_{i=1}^{m} max\left(0, 1 - y... | Is Gradient Descent possible for kernelized SVMs (if so, why do people use Quadratic Programming)? | I might be wrong, but I don't see how we can replace the dot products with kernels without turning it into the dual problem.
The kernels map the input implicitly to some feature space where $x$ become | Is Gradient Descent possible for kernelized SVMs (if so, why do people use Quadratic Programming)?
I might be wrong, but I don't see how we can replace the dot products with kernels without turning it into the dual problem.
The kernels map the input implicitly to some feature space where $x$ becomes $\phi(x)$, the loss... | Is Gradient Descent possible for kernelized SVMs (if so, why do people use Quadratic Programming)?
I might be wrong, but I don't see how we can replace the dot products with kernels without turning it into the dual problem.
The kernels map the input implicitly to some feature space where $x$ become |
10,084 | What is the upside of treating a factor as random in a mixed model? | 1. A famous example in psychology and linguistics is described by Herb Clark (1973; following Coleman, 1964): "The language-as-fixed-effect fallacy: A critique of language statistics in psychological research."
Clark is a psycholinguist discussing psychological experiments in which a sample of research subjects make re... | What is the upside of treating a factor as random in a mixed model? | 1. A famous example in psychology and linguistics is described by Herb Clark (1973; following Coleman, 1964): "The language-as-fixed-effect fallacy: A critique of language statistics in psychological | What is the upside of treating a factor as random in a mixed model?
1. A famous example in psychology and linguistics is described by Herb Clark (1973; following Coleman, 1964): "The language-as-fixed-effect fallacy: A critique of language statistics in psychological research."
Clark is a psycholinguist discussing psyc... | What is the upside of treating a factor as random in a mixed model?
1. A famous example in psychology and linguistics is described by Herb Clark (1973; following Coleman, 1964): "The language-as-fixed-effect fallacy: A critique of language statistics in psychological |
10,085 | What is the upside of treating a factor as random in a mixed model? | Suppose I have a manufacturing process that involves making material on several different machines. They're the only machines I have so "machine" is a fixed effect. But I make many lots of material on each machine and I am interested in predicting things about future lots. I'll make "Lot number" a random factor beca... | What is the upside of treating a factor as random in a mixed model? | Suppose I have a manufacturing process that involves making material on several different machines. They're the only machines I have so "machine" is a fixed effect. But I make many lots of material | What is the upside of treating a factor as random in a mixed model?
Suppose I have a manufacturing process that involves making material on several different machines. They're the only machines I have so "machine" is a fixed effect. But I make many lots of material on each machine and I am interested in predicting th... | What is the upside of treating a factor as random in a mixed model?
Suppose I have a manufacturing process that involves making material on several different machines. They're the only machines I have so "machine" is a fixed effect. But I make many lots of material |
10,086 | What is the upside of treating a factor as random in a mixed model? | So you treat them as random so that there is an averaging effect between the overall average and the average for that particular factor based on the sample size of the factor and the overall number of observations. This allows you to say that your results apply to the population at large, since you have a type of weigh... | What is the upside of treating a factor as random in a mixed model? | So you treat them as random so that there is an averaging effect between the overall average and the average for that particular factor based on the sample size of the factor and the overall number of | What is the upside of treating a factor as random in a mixed model?
So you treat them as random so that there is an averaging effect between the overall average and the average for that particular factor based on the sample size of the factor and the overall number of observations. This allows you to say that your resu... | What is the upside of treating a factor as random in a mixed model?
So you treat them as random so that there is an averaging effect between the overall average and the average for that particular factor based on the sample size of the factor and the overall number of |
10,087 | What is the upside of treating a factor as random in a mixed model? | When we have a grouping structure to our data $ Y_{ij} = \beta_1 X_{ij} + \beta_2 Z_{i} + e_{i} + \mu_{ij}$ where $X_{ij}$ are the observables of our individual observations and $Z_{i}$ are observations invariant among them and only observed at the grouping level, we cannot use fixed effects if we want to estimate $\be... | What is the upside of treating a factor as random in a mixed model? | When we have a grouping structure to our data $ Y_{ij} = \beta_1 X_{ij} + \beta_2 Z_{i} + e_{i} + \mu_{ij}$ where $X_{ij}$ are the observables of our individual observations and $Z_{i}$ are observatio | What is the upside of treating a factor as random in a mixed model?
When we have a grouping structure to our data $ Y_{ij} = \beta_1 X_{ij} + \beta_2 Z_{i} + e_{i} + \mu_{ij}$ where $X_{ij}$ are the observables of our individual observations and $Z_{i}$ are observations invariant among them and only observed at the gro... | What is the upside of treating a factor as random in a mixed model?
When we have a grouping structure to our data $ Y_{ij} = \beta_1 X_{ij} + \beta_2 Z_{i} + e_{i} + \mu_{ij}$ where $X_{ij}$ are the observables of our individual observations and $Z_{i}$ are observatio |
10,088 | What is the upside of treating a factor as random in a mixed model? | I think it's related to consistency of the estimates.
Let's say $x_{ij} = a_i+b_j+e$ where $a_i$ stands for fixed effect(some experimental condition)
and $b_j$ stands for random effect(may person).
Neyman and Scott(1948) points out the problem of consistency of
Maximum Likelihood Estimates of $a_i$ and $b_j$.
If we tak... | What is the upside of treating a factor as random in a mixed model? | I think it's related to consistency of the estimates.
Let's say $x_{ij} = a_i+b_j+e$ where $a_i$ stands for fixed effect(some experimental condition)
and $b_j$ stands for random effect(may person).
Ne | What is the upside of treating a factor as random in a mixed model?
I think it's related to consistency of the estimates.
Let's say $x_{ij} = a_i+b_j+e$ where $a_i$ stands for fixed effect(some experimental condition)
and $b_j$ stands for random effect(may person).
Neyman and Scott(1948) points out the problem of consi... | What is the upside of treating a factor as random in a mixed model?
I think it's related to consistency of the estimates.
Let's say $x_{ij} = a_i+b_j+e$ where $a_i$ stands for fixed effect(some experimental condition)
and $b_j$ stands for random effect(may person).
Ne |
10,089 | Post hoc test after ANOVA with repeated measures using R | What you could do is specify the model with lme and then use glht from the multcomp package to do what you want. However, lme gives slightly different F-values than a standard ANOVA (see also my recent questions here).
lme_velocity = lme(Velocity ~ Material, data=scrd, random = ~1|Subject)
anova(lme_velocity)
require(... | Post hoc test after ANOVA with repeated measures using R | What you could do is specify the model with lme and then use glht from the multcomp package to do what you want. However, lme gives slightly different F-values than a standard ANOVA (see also my recen | Post hoc test after ANOVA with repeated measures using R
What you could do is specify the model with lme and then use glht from the multcomp package to do what you want. However, lme gives slightly different F-values than a standard ANOVA (see also my recent questions here).
lme_velocity = lme(Velocity ~ Material, data... | Post hoc test after ANOVA with repeated measures using R
What you could do is specify the model with lme and then use glht from the multcomp package to do what you want. However, lme gives slightly different F-values than a standard ANOVA (see also my recen |
10,090 | Post hoc test after ANOVA with repeated measures using R | If you want to stick with the aov() function you can use the emmeans package which can handle aovlist (and many other) objects.
library("emmeans")
# set orthogonal contrasts
options(contrasts = c("contr.sum", "contr.poly"))
aov_velocity <- aov(Velocity ~ Material + Error(Subject / Material), data = scrd)
After crea... | Post hoc test after ANOVA with repeated measures using R | If you want to stick with the aov() function you can use the emmeans package which can handle aovlist (and many other) objects.
library("emmeans")
# set orthogonal contrasts
options(contrasts = c("c | Post hoc test after ANOVA with repeated measures using R
If you want to stick with the aov() function you can use the emmeans package which can handle aovlist (and many other) objects.
library("emmeans")
# set orthogonal contrasts
options(contrasts = c("contr.sum", "contr.poly"))
aov_velocity <- aov(Velocity ~ Mater... | Post hoc test after ANOVA with repeated measures using R
If you want to stick with the aov() function you can use the emmeans package which can handle aovlist (and many other) objects.
library("emmeans")
# set orthogonal contrasts
options(contrasts = c("c |
10,091 | Post hoc test after ANOVA with repeated measures using R | If sphericity is met then you can run a two-way ANOVA:
aov_velocity = aov(Velocity~Material+Subject, data=scrd)
posthoc = TukeyHSD(aov_velocity, 'Material', conf.level=0.95). | Post hoc test after ANOVA with repeated measures using R | If sphericity is met then you can run a two-way ANOVA:
aov_velocity = aov(Velocity~Material+Subject, data=scrd)
posthoc = TukeyHSD(aov_velocity, 'Material', conf.level=0.95). | Post hoc test after ANOVA with repeated measures using R
If sphericity is met then you can run a two-way ANOVA:
aov_velocity = aov(Velocity~Material+Subject, data=scrd)
posthoc = TukeyHSD(aov_velocity, 'Material', conf.level=0.95). | Post hoc test after ANOVA with repeated measures using R
If sphericity is met then you can run a two-way ANOVA:
aov_velocity = aov(Velocity~Material+Subject, data=scrd)
posthoc = TukeyHSD(aov_velocity, 'Material', conf.level=0.95). |
10,092 | interpreting y axis of a partial dependence plots | Each point on the partial dependence plot is the average vote percentage in favor of the "Yes trees" class across all observations, given a fixed level of TRI.
It's not a probability of correct classification. It has absolutely nothing to do with accuracy, true negatives, and true positives.
When you see the phrase
Va... | interpreting y axis of a partial dependence plots | Each point on the partial dependence plot is the average vote percentage in favor of the "Yes trees" class across all observations, given a fixed level of TRI.
It's not a probability of correct classi | interpreting y axis of a partial dependence plots
Each point on the partial dependence plot is the average vote percentage in favor of the "Yes trees" class across all observations, given a fixed level of TRI.
It's not a probability of correct classification. It has absolutely nothing to do with accuracy, true negative... | interpreting y axis of a partial dependence plots
Each point on the partial dependence plot is the average vote percentage in favor of the "Yes trees" class across all observations, given a fixed level of TRI.
It's not a probability of correct classi |
10,093 | interpreting y axis of a partial dependence plots | The partial dependence function basically gives you the "average" trend of that
variable (integrating out all others in the model). It's the shape of
that trend that is "important". You may interpret the relative range of
these plots from different predictor variables, but not the absolute
range. Hope that helps... | interpreting y axis of a partial dependence plots | The partial dependence function basically gives you the "average" trend of that
variable (integrating out all others in the model). It's the shape of
that trend that is "important". You may interp | interpreting y axis of a partial dependence plots
The partial dependence function basically gives you the "average" trend of that
variable (integrating out all others in the model). It's the shape of
that trend that is "important". You may interpret the relative range of
these plots from different predictor variab... | interpreting y axis of a partial dependence plots
The partial dependence function basically gives you the "average" trend of that
variable (integrating out all others in the model). It's the shape of
that trend that is "important". You may interp |
10,094 | interpreting y axis of a partial dependence plots | A way to look at y axis values is that they are relative to each other in the other plots. When that number is higher than in the other plots in absolute values, it means it is more important cause the impact of that variable on the output is larger.
If you are interested in the math behind partial dependence plots an... | interpreting y axis of a partial dependence plots | A way to look at y axis values is that they are relative to each other in the other plots. When that number is higher than in the other plots in absolute values, it means it is more important cause th | interpreting y axis of a partial dependence plots
A way to look at y axis values is that they are relative to each other in the other plots. When that number is higher than in the other plots in absolute values, it means it is more important cause the impact of that variable on the output is larger.
If you are interes... | interpreting y axis of a partial dependence plots
A way to look at y axis values is that they are relative to each other in the other plots. When that number is higher than in the other plots in absolute values, it means it is more important cause th |
10,095 | Full information maximum likelihood for missing data in R | Credit of this answer goes to @Joshua who gave an awesome answer when I posted this question to the R and Statistics community on Google+. I am simply pasting his answer below.
For running regression (without latent variable modeling), please read my notes typed after the quoted text.
Handling missing data with Max... | Full information maximum likelihood for missing data in R | Credit of this answer goes to @Joshua who gave an awesome answer when I posted this question to the R and Statistics community on Google+. I am simply pasting his answer below.
For running regressi | Full information maximum likelihood for missing data in R
Credit of this answer goes to @Joshua who gave an awesome answer when I posted this question to the R and Statistics community on Google+. I am simply pasting his answer below.
For running regression (without latent variable modeling), please read my notes ty... | Full information maximum likelihood for missing data in R
Credit of this answer goes to @Joshua who gave an awesome answer when I posted this question to the R and Statistics community on Google+. I am simply pasting his answer below.
For running regressi |
10,096 | Full information maximum likelihood for missing data in R | there is 2 main ways of handling missing data/records. U either delete the entire row of observation that has a missing value, or you find a way to generate this missing value. If u take the first approach, then you might end up losing a lot of data. In the second approach, you have to find a "clever" way to generate t... | Full information maximum likelihood for missing data in R | there is 2 main ways of handling missing data/records. U either delete the entire row of observation that has a missing value, or you find a way to generate this missing value. If u take the first app | Full information maximum likelihood for missing data in R
there is 2 main ways of handling missing data/records. U either delete the entire row of observation that has a missing value, or you find a way to generate this missing value. If u take the first approach, then you might end up losing a lot of data. In the seco... | Full information maximum likelihood for missing data in R
there is 2 main ways of handling missing data/records. U either delete the entire row of observation that has a missing value, or you find a way to generate this missing value. If u take the first app |
10,097 | Why there are two different logistic loss formulation / notations? | The short version
Yes
Yes
The long version
The nice thing about mathematical modeling is that it's flexible. These are indeed equivalent loss functions, but they derive from very different underlying models of the data.
Formula 1
The first notation derives from a Bernoulli probability model for $y$, which is conventi... | Why there are two different logistic loss formulation / notations? | The short version
Yes
Yes
The long version
The nice thing about mathematical modeling is that it's flexible. These are indeed equivalent loss functions, but they derive from very different underlyin | Why there are two different logistic loss formulation / notations?
The short version
Yes
Yes
The long version
The nice thing about mathematical modeling is that it's flexible. These are indeed equivalent loss functions, but they derive from very different underlying models of the data.
Formula 1
The first notation de... | Why there are two different logistic loss formulation / notations?
The short version
Yes
Yes
The long version
The nice thing about mathematical modeling is that it's flexible. These are indeed equivalent loss functions, but they derive from very different underlyin |
10,098 | Why there are two different logistic loss formulation / notations? | I think @ssdecontrol had a very good answer. I just want to add some comments for the formula 2 for my own question.
$$
L(y,\hat y)=\log(1+\exp{(-y\cdot \hat y}))
$$
The reason people like this formulation is that it is very concise, and it removes the "probability interpretation details".
The tricky notation is the $\... | Why there are two different logistic loss formulation / notations? | I think @ssdecontrol had a very good answer. I just want to add some comments for the formula 2 for my own question.
$$
L(y,\hat y)=\log(1+\exp{(-y\cdot \hat y}))
$$
The reason people like this formul | Why there are two different logistic loss formulation / notations?
I think @ssdecontrol had a very good answer. I just want to add some comments for the formula 2 for my own question.
$$
L(y,\hat y)=\log(1+\exp{(-y\cdot \hat y}))
$$
The reason people like this formulation is that it is very concise, and it removes the ... | Why there are two different logistic loss formulation / notations?
I think @ssdecontrol had a very good answer. I just want to add some comments for the formula 2 for my own question.
$$
L(y,\hat y)=\log(1+\exp{(-y\cdot \hat y}))
$$
The reason people like this formul |
10,099 | How to interpret Matthews correlation coefficient (MCC)? | This question was so simple and unfortunately no one could answer this question.
According to this paper: http://www.bioinfopublication.org/files/articles/2_1_1_JMLT.pdf, MCC is a contingency matrix method of calculating the
Pearson product-moment correlation coefficient. Therefore, it has the same interpretation. | How to interpret Matthews correlation coefficient (MCC)? | This question was so simple and unfortunately no one could answer this question.
According to this paper: http://www.bioinfopublication.org/files/articles/2_1_1_JMLT.pdf, MCC is a contingency matrix m | How to interpret Matthews correlation coefficient (MCC)?
This question was so simple and unfortunately no one could answer this question.
According to this paper: http://www.bioinfopublication.org/files/articles/2_1_1_JMLT.pdf, MCC is a contingency matrix method of calculating the
Pearson product-moment correlation coe... | How to interpret Matthews correlation coefficient (MCC)?
This question was so simple and unfortunately no one could answer this question.
According to this paper: http://www.bioinfopublication.org/files/articles/2_1_1_JMLT.pdf, MCC is a contingency matrix m |
10,100 | How to interpret Matthews correlation coefficient (MCC)? | Matthews Correlation Coefficient is a special case of Pearson Correlation Coefficient. Therefore, the interpretations for both of them are the same. Check the derivations and other details in my blog post on github. | How to interpret Matthews correlation coefficient (MCC)? | Matthews Correlation Coefficient is a special case of Pearson Correlation Coefficient. Therefore, the interpretations for both of them are the same. Check the derivations and other details in my blog | How to interpret Matthews correlation coefficient (MCC)?
Matthews Correlation Coefficient is a special case of Pearson Correlation Coefficient. Therefore, the interpretations for both of them are the same. Check the derivations and other details in my blog post on github. | How to interpret Matthews correlation coefficient (MCC)?
Matthews Correlation Coefficient is a special case of Pearson Correlation Coefficient. Therefore, the interpretations for both of them are the same. Check the derivations and other details in my blog |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.