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Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
2
votes
Accepted
Problem using MLP function from package RSNNS in R
model_mlp<-mlp(x,y,size=c(1),maxit=1000,learnFunc="Rprop",linOut=TRUE)
predictions_mlp<-predict(model_mlp,t(t(x))) # now gives 500 unique predictions
Backpropagation seems extremely sensitive to t …
0
votes
Accepted
Optimizing False Negative Rate after Logistic Regression
This threshold is always a trade off. Take a look at Reciever Operating Charateristic (ROC cruves). Try the R library ROCR too.
5
votes
1
answer
665
views
Account for multiple treatments to the same subject when the treatments are a numerical vari...
16, p-value < 2.2e-16
I'd like to do something like a repeated measures anova test which accounts for the fact that the same person was put through multiple samples (\$699, \$799,....) and a linear regression …
1
vote
Cross-validation and feature selection of a multivariate regression
For example stepwise regression on just $R^2$ may not be useful, but stepwise regression on AIC may be useful. … In general I remove correlated features before doing stepwise regression (although in theory stepwise regression using AIC shouldn't pull in two correlated features). …
1
vote
Machine learning for multi-level response
When you are dealing with many class problems like this, some of the classes will likely be not well represented. If you are really interested in getting these "not-well-represented" classes right, a …