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Results tagged with machine-learning
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user 17414
Machine learning algorithms build a model of the training data. The term "machine learning" is vaguely defined; it includes what is also called statistical learning, reinforcement learning, unsupervised learning, etc. ALWAYS ADD A MORE SPECIFIC TAG.
4
votes
Not clear why adding additional features (not just transformations) reduces model bias in st...
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 …
- 4,603
2
votes
Forward search feature selection and cross-validation
Your second procedure assumes you have some other feature selection algorithm (for example, stepwise regression with some stopping rule), distinct from the cross-validation. If you don't have this, yo …
- 4,603
13
votes
Accepted
Can (some) linear regression model this (population) function accurately?
I wouldn't say the authors are wrong as such, but they weren't adequately careful with the wording. Often it's clear from context whether you mean simple or multiple linear regression, but here it was …
- 4,603
9
votes
Two worlds collide: Using ML for complex survey data
Update May 2022: In terms of accounting for survey weights, there's a nice pair of recent (2020?) articles on arXiv by Dagdoug, Goga, and Haziza. They list many ML-flavored methods and discuss how the …
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3
votes
KNN K = 1 Training on itself vs K > 1
In both the $K=1$ and $K>1$ cases, if you ask KNN to classify a training point, it does include that training point as one of the neighbors.
In the left image, the black X is not one of the training p …
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1
vote
Textbook on high-dimensional statistics
You mention interest in genomic problems, with 1000s of genes but 100s of samples. Brad Efron's "Large Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction" might be a good …
4
votes
Accepted
What are the differences and common points, if any, between oversampling as a survey design ...
You are correct about oversampling in survey design. If you want to read more about it, another useful search terms is "stratified sampling" (which is itself another term that survey stats and ML use …
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3
votes
In Bishop's textbook, is the example of overfitting exaggerated?
A polynomial of 9th order needs to have 8 bumps.
That's all it needs. So Bishop's curve could simply look like the black curve which I drawn.
When we eyeball a curve, we tend to draw something more …
- 4,603
5
votes
Is it really so bad to do SMOTE on the training set before crossvalidation?
I understand that doing this leads to data leakage, but if I get better performance on the test set does it really matter?
YES, it matters! The problem with data leakage is that you can no longer tr …
- 4,603
4
votes
Variance of $K$-fold cross-validation estimates as $f(K)$: what is the role of "stability"?
This answer focuses not on stability, but on a different related issue that I have not seen addressed in the answers/comments above.
There is "conventional wisdom" about LOOCV having higher variance, …
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