For example: library(mlbench)īh_index <- createDataPartition(BostonHousing $medv, p =. The function postResample can be used to estimate the root mean squared error (RMSE), simple R 2, and the mean absolute error (MAE) for numeric outcomes. 22.2 Internal and External Performance Estimates.22 Feature Selection using Simulated Annealing.21.2 Internal and External Performance Estimates.21 Feature Selection using Genetic Algorithms.20.3 Recursive Feature Elimination via caret.20.2 Resampling and External Validation.19 Feature Selection using Univariate Filters.18.1 Models with Built-In Feature Selection.16.6 Neural Networks with a Principal Component Step.16.2 Partial Least Squares Discriminant Analysis.16.1 Yet Another k-Nearest Neighbor Function.13.9 Illustrative Example 6: Offsets in Generalized Linear Models.13.8 Illustrative Example 5: Optimizing probability thresholds for class imbalances.13.7 Illustrative Example 4: PLS Feature Extraction Pre-Processing.13.6 Illustrative Example 3: Nonstandard Formulas.13.5 Illustrative Example 2: Something More Complicated - LogitBoost.13.2 Illustrative Example 1: SVMs with Laplacian Kernels.12.1.2 Using additional data to measure performance.12.1.1 More versatile tools for preprocessing data.11.4 Using Custom Subsampling Techniques.7.0.27 Multivariate Adaptive Regression Splines.5.9 Fitting Models Without Parameter Tuning.5.8 Exploring and Comparing Resampling Distributions.5.7 Extracting Predictions and Class Probabilities.
5.1 Model Training and Parameter Tuning.4.4 Simple Splitting with Important Groups.
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