6 votes

Regression coefficient on a triangle using geometry

Because $(X,Y)$ has a uniform distribution over the triangle shown, the expectation of $Y$ conditional on $X$ evidently splits the lower and upper boundaries of the triangle, shown as the dotted line $...
whuber's user avatar
  • 316k
4 votes

How to interpret the deviance plot by boosting models

Deviance is a measure of model quality typically (but, I suppose, not necessarily) related to the likelihood. The lower the deviance, the better the model fit. Perhaps think of it this way: models are ...
Dave's user avatar
  • 58.1k
3 votes

Not clear why adding additional features (not just transformations) reduces model bias in statistical machine learning

You wrote: With one feature we will have a straight line in a plain, and by adding one feature we will have a straight plane in a 3 dimensional space. Imagine that your response variable, $Y$, truly ...
civilstat's user avatar
  • 3,442
3 votes

How to combine two linear models?

Sure. Let $s$ be the feature that represents the square-footage of the house, and $v$ a feature vector containing all other features. Then I claim that every statistical model that has no jumps at $...
D.W.'s user avatar
  • 6,578
2 votes

Why use the EM Algorithm and not just marginalise the complete likelihood?

This is a few years old but I don't believe the current answer really covers it. What the question boils down is whether $\mathbb{E}_{p(z\mid x;\theta')}[\log p(x, z\,;\, \theta')]$ (in EM lower-bound)...
Lulu's user avatar
  • 168
1 vote

How to combine two linear models?

I was thinking of this way to estimate the two models as one. It's like D.W.'s suggestion, but specified differently. Consider using: ...
BenP's user avatar
  • 163
1 vote

Is pretraining on test set texts (without labels) ok?

TLDR: seems fine, which is weird. I'll check that the experiment code is correct. This answer contains empirical experiments similar to my experiment with PCA here. We'll run a few experiments with ...
chicxulub's user avatar
  • 1,260

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