Man pages for zachmayer/caretEnsemble
Ensembles of Caret Models
| aggregate_mean_or_first | Aggregate mean or first |
| as.caretList | Convert object to caretList object |
| as.caretList.default | Convert object to caretList object - For Future Use |
| as.caretList.list | Convert list to caretList |
| autoplot.caretStack | Convenience function for more in-depth diagnostic plots of... |
| caretEnsemble | Combine several predictive models via weights |
| caretList | Create a list of several train models from the caret package |
| caretModelSpec | Generate a specification for fitting a caret model |
| caretPredict | Prediction wrapper for 'train' |
| caretStack | Combine several predictive models via stacking |
| caretTrain | Wrapper to train caret models |
| c.caretList | S3 definition for concatenating caretList |
| check_caretStack | Check caretStack object |
| checkCustomModel | Validate a custom caret model info list |
| c.train | S3 definition for concatenating train objects |
| defaultControl | Construct a default train control for use with caretList |
| defaultMetric | Construct a default metric |
| dotplot.caretStack | Comparison dotplot for a caretStack object |
| dropExcludedClass | Drop Excluded Class |
| extractBestPreds | Extract the best predictions from a train object |
| extractCaretTarget | Extracts the target variable from a set of arguments headed... |
| extractCaretTarget.default | Extracts the target variable from a set of arguments headed... |
| extractCaretTarget.formula | Extracts the target variable from a set of arguments headed... |
| extractMetric | Generic function to extract accuracy metrics from various... |
| extractMetric.caretList | Extract accuracy metrics from a 'caretList' object |
| extractMetric.caretStack | Extract accuracy metrics from a 'caretStack' object |
| extractMetric.train | Extract accuracy metrics from a 'train' model |
| extractModelName | Extract the method name associated with a single train object |
| greedyMSE | Greedy optimization for MSE |
| greedyMSE_caret | caret interface for greedyMSE |
| isClassifier | Is Classifier |
| isClassifierAndValidate | Validate a model type |
| mae | Compute MAE |
| methodCheck | Check that the methods supplied by the user are valid caret... |
| models.class | caretList of classification models |
| models.reg | caretList of regression models |
| normalize_to_one | Normalize to One |
| permutationImportance | Permutation Importance |
| plot.caretList | Plot a caretList object |
| plot.caretStack | Plot a caretStack object |
| predict.caretList | Create a matrix of predictions for each of the models in a... |
| predict.caretStack | Make predictions from a caretStack |
| predict.greedyMSE | Predict method for greedyMSE |
| print.caretStack | Print a caretStack object |
| print.greedyMSE | Print method for greedyMSE |
| print.summary.caretList | Print a summary.caretList object |
| print.summary.caretStack | Print a summary.caretStack object |
| set_excluded_class_id | Set excluded class id |
| shuffled_mae | Shuffled MAE |
| stackedTrainResiduals | Extracted stacked residuals for the autoplot |
| sub-.caretList | Index a caretList |
| summary.caretList | Summarize a caretList |
| summary.caretStack | Summarize a caretStack object |
| tuneCheck | Check that the tuning parameters list supplied by the user is... |
| validateExcludedClass | Validate the excluded class |
| varImp.caretStack | Variable importance for caretStack |
| varImp.greedyMSE | variable importance for a greedyMSE model |
| wtd.sd | Calculate a weighted standard deviation |
| X.class | data for classification |
| X.reg | data for classification |
| Y.class | data for classification |
| Y.reg | data for regression |
