Building Regression and Classification Models
Best Cutpoint for a ROC Curve
Breusch-Pagan Test
Confidence Interval for the Difference of Two Coefficients in a Linear...
Complexity Parameter of an rpart Model
Wrapper for Several Model Functions
Leafrates for the Nodes of an 'rpart' Tree
LogitBoost Classification Algorithm
Regression and Classification Tools
Nodes and Splits in an rpart Tree
Oversample and Undersample
Lift Charts to Compare Binary Predictive Models
Confidence Intervals for Predictions of a GLM
Used Reference Levels in a Linear Model
Extract the Response from Several Models
Robust Summary for Linear Models
Build a ROC curve
Extract Rules from 'rpart' Object
Split DataFrame in Train an Test Sample
Compare Classification Models
Tobit Regression
Tune Classificators
Variable Importance for Regression and Classification Models
Consistent user interface to the most common regression and classification algorithms, such as random forest, neural networks, C5 trees and support vector machines, complemented with a handful of auxiliary functions, such as variable importance and a tuning function for the parameters.
Useful links