Classification and Regression Training
Confusion matrix as a table
Neural Networks Using Model Averaging
A General Framework For Bagging
Bagged Earth
Bagged FDA
Box-Cox and Exponential Transformations
Probability Calibration Plot
Internal Functions
Backwards Feature Selection Helper Functions
Selection By Filtering (SBF) Helper Functions
Compute and predict the distances to class centroids
Create a confusion matrix
Estimate a Resampled Confusion Matrix
Data Splitting functions
Lattice functions for plotting resampling results of recursive feature...
Inferential Assessments About Model Performance
Lattice Functions for Visualizing Resampling Differences
Create a dotplot of variable importance values
Down- and Up-Sampling Imbalanced Data
Create A Full Set of Dummy Variables
Wrapper for Lattice Plotting of Predictor Variables
Calculation of filter-based variable importance
Determine highly correlated variables
Determine linear combinations in a matrix
Format 'bagEarth' objects
Genetic algorithm feature selection
Ancillary genetic algorithm functions
Get sampling info from a train model
Lattice functions for plotting resampling results
Independent Component Regression
Convert indicies to a binary vector
k-Nearest Neighbour Classification
k-Nearest Neighbour Regression
Create Data to Plot a Learning Curve
Lift Plot
Maximum Dissimilarity Sampling
Tools for Models Available in train
A List of Available Models in train
Identification of near zero variance predictors
Fit a simple, non-informative model
Selecting tuning Parameters
Lattice Panel Functions for Lift Plots
Needle Plot Lattice Panel
Neural Networks with a Principal Component Step
Plot Method for the gafs and safs Classes
Plot RFE Performance Profiles
Plot Method for the train Class
Plotting variable importance measures
Plot Predicted Probabilities in Classification Models
Plot Observed versus Predicted Results in Regression and Classificatio...
Partial Least Squares and Sparse Partial Least Squares Discriminant An...
Calculates performance across resamples
Principal Components Analysis of Resampling Results
Predicted values based on bagged Earth and FDA models
Predict new samples
Predictions from k-Nearest Neighbors
Predictions from k-Nearest Neighbors Regression Model
Extract predictions and class probabilities from train objects
List predictors used in the model
Pre-Processing of Predictors
Print method for confusionMatrix
Print Method for the train Class
Calculate recall, precision and F values
Plot the resampling distribution of the model statistics
Collation and Visualization of Resampling Results
Summary of resampled performance estimates
Backwards Feature Selection
Controlling the Feature Selection Algorithms
Simulated annealing feature selection
Ancillary simulated annealing functions
Control parameters for GA and SA feature selection
Selection By Filtering (SBF)
Control Object for Selection By Filtering (SBF)
Calculate sensitivity, specificity and predictive values
Compute the multivariate spatial sign
Summarize a bagged earth or FDA fit
Generate Data to Choose a Probability Threshold
Fit Predictive Models over Different Tuning Parameters
Control parameters for train
Simulation Functions
Update or Re-fit a SA or GA Model
Update or Re-fit a Model
Sequences of Variables for Tuning
Variable importances for GAs and SAs
Calculation of variable importance for regression and classification m...
Lattice Functions for Visualizing Resampling Results
Misc functions for training and plotting classification and regression models.