BMI at baseline & followup with exposure at baseline; model change or BMI at FUP? Control for BMI baseline?
For a prospective occupational cohort where everyone is exposed to one or more chemical agents, examining BMI at follow-up compared to a specific chemical exposure at baseline, is it necessary to ...
If I train a GNB/LDA/kNN/other classifier I would like to know, in the model built, how important are features to classify or which feature(s) drives the classifier. For example in SVM models the ...
What are some common tools, initial approaches to data in a prediction problem when facing too many predictors?
If one is given several hundred features (of both categorical and continuous type) what are some approaches to determining which features to keep or even drop? Data as such is difficult to visualize ...
Variables are often adjusted (e.g. standardised) before making a model - when is this a good idea, and when is it a bad one?
In what circumstances would you want to, or not want to scale or standardize a variable prior to model fitting? And what are the advantages / disadvantages of scaling a variable?
Common data-based variable selection procedures (for example, forward, backward, stepwise, all subsets) tend to yield models with undesirable properties, including: Coefficients biased away from ...
I don't know enough math to formulate an intelligent question on this so I'll give an example. I'd like an answer to my example but also I'd like to know the jargon I need to be able to research it ...
For the selection of predictors in multivariate linear regression with $p$ suitable predictors, what methods are available to find an 'optimal' subset of the predictors without explicitly testing all ...