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Blood Brain Barrier Data

Box-Cox and Exponential Transformations

German Credit Data

Sacramento CA Home Prices

Confusion matrix as a table

Neural Networks Using Model Averaging

A General Framework For Bagging

Bagged Earth

Bagged FDA

Probability Calibration Plot

Internal Functions

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

Create a confusion matrix

Estimate a Resampled Confusion Matrix

COX-2 Activity Data

Data Splitting functions

Lattice functions for plotting resampling results of recursive feature selection

Dihydrofolate Reductase Inhibitors Data

Inferential Assessments About Model Performance

Create a dotplot of variable importance values

Lattice Functions for Visualizing Resampling Differences

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

Multidrug Resistance Reversal (MDRR) Agent Data

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

Fatty acid composition of commercial oils

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 Classification Models

Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis

Calculates performance across resamples

Pottery from Pre-Classical Sites in Italy

Principal Components Analysis of Resampling Results

Pre-Processing of Predictors

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

Print method for confusionMatrix

Print Method for the train Class

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

Backwards Feature Selection

Controlling the Feature Selection Algorithms

Simulated annealing feature selection

Control parameters for GA and SA feature selection

Ancillary simulated annealing functions

Selection By Filtering (SBF)

Control Object for Selection By Filtering (SBF)

Morphometric Data on Scat

Cell Body Segmentation

Calculate sensitivity, specificity and predictive values

Compute the multivariate spatial sign

Summarize a bagged earth or FDA fit

Fat, Water and Protein Content of Meat Samples

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

Calculation of variable importance for regression and classification models

Variable importances for GAs and SAs

Sequences of Variables for Tuning

Lattice Functions for Visualizing Resampling Results