Tagged Questions

0
votes
1answer
137 views

AIC, BIC, DIC, model selection criteria

I am trying to understand the difference between these parameters, and their application. Was hoping to get some correction/clarification to my statements. I have a training set and cross-validation ...
3
votes
0answers
115 views

Sensible to include ratio as a variable in logistic regression?

I'm creating a generalised linear regression using a binomial link function for two variables A and B. From looking at the data it appears that A/B may have discriminatory effect. Is it sensible to ...
4
votes
1answer
344 views

Variable selection with LASSO

I am trying to fit a predictive gene-based model in survival analysis. My question is: Can I use LASSO as a variable selection method, and then run a multivariate Cox regression to get the ...
1
vote
0answers
136 views

Guassian Process Regression - feature selection

I'm using guassian process regression to do some modeling. One issue I'm encountering is feature selection for some of my models, which often have many relevant features. I'm not sure what the best ...
1
vote
0answers
53 views

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 ...
2
votes
0answers
86 views

Non-linear (e.g. RBF kernel) SVM with SCAD penalties implementation

Is there one? I think there's a penalizedSVM package in R but it looks to use a linear kernel. Can't quite tell from the documentation. If it's linear, is there a R package that lets me calculate the ...
9
votes
2answers
169 views

Domain-agnostic feature engineering that retains semantic meaning?

Feature engineering is often an important component to machine learning (it was used heavily to win the KDD Cup in 2010). However, I find that most feature engineering techniques either destroy ...
12
votes
3answers
1k views

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?