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I only read the first question in theory.md and think the answer is quite weak. > What is regression? Which models can you use to solve a regression problem?

I only read the first question in theory.md and think the answer is quite weak. > What is regression? Which models can you use to solve a regression problem? The current answer only list some names that have "regression" in it, and the description of what a regression is doesn't say anything that distinguishes it from classification. It fails to mention that regression (in ML terminology) is prediction of a continuous variable. And that almost any method can be used to do regression: knn, neural network, random forest, svm. If other answers are of similar nature you might fail the interview.

dgellow 6y ago
From the README: > The answers are given by the community > If you know how to answer a question — please create a PR with the answer > If there's already an answer, but you can improve it — please create a PR with improvement suggestion > If you see a mistake — please create a PR with a fix
- kristjansson 6y ago
  I don’t like this response. It’s completely reasonable to assess the quality of informational content without assuming a duty to proofread and revise that content. Especially when the issue that might be corrected isn’t a typo or malapropism but ‘this whole thing isn’t that great.’
  - dgellow 6y ago
    Sure, I’m not saying that you have to correct it. Just that the project doesn’t assume that they have correct answers for everything and expect some participation of “the community” at large.
atty 6y ago
I read two answers from technical.md, and one of them was inconsistent with what was asked for... I would be very suspicious of using this repo for studying.
- pw6hv 6y ago
  Could you elaborate on which one in particular? I've also read the first questions but haven't noticed anything like this.
  - atty 6y ago
    Sorry, I should have put it in my original reply. If you search for where it asks for an implementation of the standard deviation, you'll see that it asks for an implementation including Bessel's correction (using N-1 instead of N for the normalization, used to correct for the one degree of freedom lost by the calculation of the mean). However, when you look at the answer, provided, you'll notice that it divides by N, and does not provide 'NaN' for the condition N=1, as was asked.
savagedata 6y ago
One of my favorite data science factoids is how "regression" (to return to a former state) came to mean "prediction of a continuous variable". In the late 1800s, Sir Francis Galton noticed that extremely tall or short parents usually had children that were not as tall or short as themselves, i.e. the children's heights were regressing (returning) to the mean. He collected hundreds of data points, graphed them, and estimated a coefficient describing this relationship, thereby inventing "linear regression." We call them "regression" models simply because the first linear regression model was created to demonstrate the concept of regression to the mean. https://en.wikipedia.org/wiki/Regression_toward_the_mean#History https://en.wikipedia.org/wiki/Regression_toward_the_mean#His...
notafraudster 6y ago
The error here is ML terminology; the article answers correctly, if shallowly, the statistical definition of regression. Classical regression techniques can be (and are correctly) used on binary, ordinal, or categorical dependent variables. I know we teach people doing ML that the two forms of supervised ML are classification and regression, but that does a disservice mostly in order to make visual examples in teaching easier by making every topic a binary classification question and then saying "oh yeah, this works for regression too". Granted in an interview you'd probably want to use context in case the hiring people were trained on a specific vocab, but that maybe speaks more to the folly of using these dial-an-answer systems in place of actually learning the material.

dgellow 6y ago

From the README: > The answers are given by the community > If you know how to answer a question — please create a PR with the answer > If there's already an answer, but you can improve it — please create a PR with improvement suggestion > If you see a mistake — please create a PR with a fix

kristjansson 6y ago
I don’t like this response. It’s completely reasonable to assess the quality of informational content without assuming a duty to proofread and revise that content. Especially when the issue that might be corrected isn’t a typo or malapropism but ‘this whole thing isn’t that great.’
- dgellow 6y ago
  Sure, I’m not saying that you have to correct it. Just that the project doesn’t assume that they have correct answers for everything and expect some participation of “the community” at large.

atty 6y ago

I read two answers from technical.md, and one of them was inconsistent with what was asked for... I would be very suspicious of using this repo for studying.

pw6hv 6y ago
Could you elaborate on which one in particular? I've also read the first questions but haven't noticed anything like this.
- atty 6y ago
  Sorry, I should have put it in my original reply. If you search for where it asks for an implementation of the standard deviation, you'll see that it asks for an implementation including Bessel's correction (using N-1 instead of N for the normalization, used to correct for the one degree of freedom lost by the calculation of the mean). However, when you look at the answer, provided, you'll notice that it divides by N, and does not provide 'NaN' for the condition N=1, as was asked.

savagedata 6y ago

One of my favorite data science factoids is how "regression" (to return to a former state) came to mean "prediction of a continuous variable". In the late 1800s, Sir Francis Galton noticed that extremely tall or short parents usually had children that were not as tall or short as themselves, i.e. the children's heights were regressing (returning) to the mean. He collected hundreds of data points, graphed them, and estimated a coefficient describing this relationship, thereby inventing "linear regression." We call them "regression" models simply because the first linear regression model was created to demonstrate the concept of regression to the mean. https://en.wikipedia.org/wiki/Regression_toward_the_mean#History https://en.wikipedia.org/wiki/Regression_toward_the_mean#His...

notafraudster 6y ago

The error here is ML terminology; the article answers correctly, if shallowly, the statistical definition of regression. Classical regression techniques can be (and are correctly) used on binary, ordinal, or categorical dependent variables. I know we teach people doing ML that the two forms of supervised ML are classification and regression, but that does a disservice mostly in order to make visual examples in teaching easier by making every topic a binary classification question and then saying "oh yeah, this works for regression too". Granted in an interview you'd probably want to use context in case the hiring people were trained on a specific vocab, but that maybe speaks more to the folly of using these dial-an-answer systems in place of actually learning the material.