nestcv.glmnet, nestcv.train, outercv and repeatcv (thanks to Ryan Thompson for useful code for repeatcv).multicore_fork has been removed.pred_nestcv_glmnet_class() and pred_train_class() (thanks to SamGG).inner_folds in nestcv.train() (thanks to Ryan Thompson).repeatcv to enable return of fitted models from the outer CV for variable importance or SHAP value calculation.defaultSummary() function from caret which uses the square of Pearson correlation coefficient (r-squared), instead of the correct coefficient of determination which is calculated as 1 - rss/tss, where rss = residual sum of squares, tss = total sum of squares. The correct formula for R-squared is now being applied.x is a single predictor.prc() which enables easy building of precision-recall curves from ânestedcvâ models and repeatcv() results.predict method for cva.glmnet.|> can be used instead.metrics() which gives additional performance metrics for binary classification models such as F1 score, Matthewâs correlation coefficient and precision recall AUC.pls_filter() which uses partial least squares regression to filter features.repeatedcv() leading to significant improvement in speed.nestcv.train(). If argument cv.cores >1, openMP multithreading is now disabled, which prevents caret models xgbTree and xgbLinear from crashing, and allows them to be parallelised efficiently over the outer CV loops.var_stability() and its plots.nestcv.glmnet()repeatcv() to apply repeated nested CV to the main nestedcv model functions for robust measurement of model performance.modifyX argument to all nestedcv models. This allows more powerful manipulation of the predictors such as scaling, imputing missing values, adding extra columns through variable manipulations. Importantly these are applied to train and test input data separately.predict() function for nestcv.SuperLearner()pred_SuperLearner wrapper for use with fastshap::explainnestcv.SuperLearner() on windows.nestcv.glmnet()verbose in nestcv.train(), nestcv.glmnet() and outercv()to show progress.multicore_fork in nestcv.train() and outercv() to allow choice of parallelisation between forked multicore processing using mclapply or non-forked using parLapply. This can help prevent errors with certain multithreaded caret models e.g. model = "xgbTree".one_hot() changed all_levels argument default to FALSE to be compatible with regression models by default.lm_filter() full results tablelm_filter() where variables with zero variance were incorrectly reporting very low p-values in linear models instead of returning NA. This is due to how rank deficient models are handled by RcppEigen::fastLmPure. Default method for fastLmPure has been changed to 0 to allow detection of rank deficient models.weight() caused by NA. Allow weight() to tolerate character vectors.keep_factors option has been added to filters to control filtering of factors with 3 or more levels.one_hot() for fast one-hot encoding of factors and character columns by creating dummy variables.stat_filter() which applies univariate filtering to dataframes with mixed datatype (continuous & categorical combined).anova_filter() from Rfast::ftests() to matrixTests::col_oneway_welch() for much better accuracynestcv.train() (Matt Siggins suggestion)n_inner_folds argument to nestcv.train() to make it easier to set the number of inner CV folds, and inner_folds argument which enables setting the inner CV fold indices directly (suggestion Aline Wildberger)plot_shap_beeswarm() caused by change in fastshap 0.1.0 output from tibble to matrixnestcv.train()pass_outer_folds to both nestcv.glmnet and nestcv.train: this enables passing of passing of outer CV fold indices stored in outer_folds to the final round of CV. Note this can only work if n_outer_folds = number of inner CV folds and balancing is not applied so that y is a consistent length.nfolds for final CV equals n_inner_folds in nestcv.glmnet()plot_var_stability() to be more user friendlytop argument to shap plotsfastshap for calculating SHAP values.force_vars argument to glmnet_filter()ranger_filter()nestcv.train() from models such as gbm. This fixes multicore bug when using standard R gui on mac/linux.nestcv.glmnet() model has 0 or 1 coefficients.nestedcv models now return xsub containing a subset of the predictor matrix x with filtered variables across outer folds and the final fitboxplot_model() no longer needs the predictor matrix to be specified as it is contained in xsub in nestedcv modelsboxplot_model() now works for all nestedcv model typesvar_stability() to assess variance and stability of variable importance across outer folds, and directionality for binary outcomeplot_var_stability() to plot variable stability across outer foldsfinalCV = NA option which skips fitting the final model completely. This gives a useful speed boost if performance metrics are all that is needed.model argument in outercv now prefers a character value instead of a function for the model to be fittedoutercvnestcv.train which improves error detection in caret. So nestcv.train can be run in multicore mode straightaway.nestcv.glmnetnestcv.glmnetouter_train_predict argument to enable saving of predictions on outer training foldstrain_preds to obtain outer training fold predictionstrain_summary to show performance metrics on outer training foldssmote()SuperLearner packagenestcv.train and nestcv.glmnetnestcv.train for caret models with tuning parameters which are factorsnestcv.train for caret models using regressionnestcv.train and nestcv.glmnet to tune final model parameters using a final round of CV on the whole datasetnestcv.train and outercvrandomsample() to handle class imbalance using random over/undersamplingsmote() for SMOTE algorithm for increasing minority class databoot_ttest()nestcv.glmnet() is mean of best lambdas on log scaleplot_varImp for plotting variable importance for nestcv.glmnet final modelsnestcv.glmnet()cva.glmnet()plot.cva.glmnetalphaSet in plot.cva.glmnettrain function of caretfilterFUN is no longer done through ... but with a list of arguments passed through a new argument filter_options.RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4