fertwords.blogg.se

This method does not currently provide class-specific measures of importance when the response is a factor. This can be turned off using the maxcompete argument in ntrol. Also, since there may be candidate variables that are important but are not used in a split, the top competing variables are also tabulated at each split. Recursive Partitioning: The reduction in the loss function (e.g. mean squared error) attributed to each variable at each split is tabulated and the sum is returned.Therefore, the contribution of the coefficients are weighted proportionally to the reduction in the sums of squares. The weights are a function of the reduction of the sums of squares across the number of PLS components and are computed separately for each outcome. Partial Least Squares: the variable importance measure here is based on weighted sums of the absolute regression coefficients.If the standard error is equal to 0 for a variable, the division is not done.” The differences are averaged and normalized by the standard error.

For regression, the MSE is computed on the out-of-bag data for each tree, and then the same computed after permuting a variable. The difference between the two accuracies are then averaged over all trees, and normalized by the standard error. Then the same is done after permuting each predictor variable.

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.4.1 Simple Splitting Based on the Outcome.3.2 Zero- and Near Zero-Variance Predictors.