If pruning is not used, the ensemble makes predictions using the exact value of the mstop tuning parameter value. Notes: The prune option for this model enables the number of iterations to be determined by the optimal AIC value across all iterations. Notes: Unlike other packages used by train, the earth package is fully loaded when this model is used.īagged MARS using gCV Pruning method = 'bagEarthGCV'īayesian Generalized Linear Model method = 'bayesglm'īoosted Generalized Additive Model method = 'gamboost' Multivariate Adaptive Regression SplinesĪdjacent Categories Probability Model for Ordinal Data method = 'vglmAdjCat'Ī model-specific variable importance metric is available.īagged Flexible Discriminant Analysis method = 'bagFDA'Ī model-specific variable importance metric is available.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.It might be hard to get at adjusted R2 in this way, but you could probably get R2 or something similar. (or maximize, see the maximize argument to train), given a predictor and a response. 5.1 Model Training and Parameter Tuning I am interested in utilizing caret for making inferences on a particular data set.4.4 Simple Splitting with Important Groups.4.1 Simple Splitting Based on the Outcome. ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |