Backwards Feature Selection Helper Functions
Selection By Filtering (SBF) Helper Functions
Kelly Blue Book resale data for 2005 model year GM cars
Compute and predict the distances to class centroids
Lattice functions for plotting resampling results of recursive feature selection
Inferential Assessments About Model Performance
Create a dotplot of variable importance values
Lattice Functions for Visualizing Resampling Differences
Wrapper for Lattice Plotting of Predictor Variables
Calculation of filter-based variable importance
Determine linear combinations in a matrix
Lattice functions for plotting resampling results
Multidrug Resistance Reversal (MDRR) Agent Data
Identification of near zero variance predictors
Fatty acid composition of commercial oils
Neural Networks with a Principal Component Step
Plot Method for the gafs and safs Classes
Plot Predicted Probabilities in Classification Models
Plot Observed versus Predicted Results in Regression and Classification Models
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
Pottery from Pre-Classical Sites in Italy
Principal Components Analysis of Resampling Results
Predicted values based on bagged Earth and FDA models
Predictions from k-Nearest Neighbors Regression Model
Extract predictions and class probabilities from train objects
Calculate recall, precision and F values
Plot the resampling distribution of the model statistics
Summary of resampled performance estimates
Collation and Visualization of Resampling Results
Controlling the Feature Selection Algorithms
Control parameters for GA and SA feature selection
Control Object for Selection By Filtering (SBF)
Calculate sensitivity, specificity and predictive values
Fat, Water and Protein Content of Meat Samples
Generate Data to Choose a Probability Threshold
Fit Predictive Models over Different Tuning Parameters
Calculation of variable importance for regression and classification models
Lattice Functions for Visualizing Resampling Results