6
votes
Accepted
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 $...
- 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 ...
- 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 ...
- 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 $...
- 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)...
- 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:
...
- 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 ...
- 1,260
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