rtemis

• Variable importance (get_varimp()) holds one measure per name, each stating what it quantifies (split gain, permutation, coefficient, ...), the cases it was computed on, whether it is signed or depends on predictor units, how it ranks predictors, and a description for the fitted model; the measures replace the single table in @data, and varimp_table() combines them into one data.frame. plot_varimp() ranks bars by the measure's direction (a cross-validated risk smallest first). Linear SVM weights are oriented toward the positive class, and a forest fitted with importance = "none" has no importance.
• writeup() writes the Methods and Results sections for a trained supervised model from the model and its review(), every number traceable to the field it was read from, with references to R, rtemis, the fitting packages and the statistical methods, a main hyperparameter table (primary hyperparameters and every one tuned, specified or resolved during fitting; include_hyperparameters replaces the primary list) and a supplementary table of every hyperparameter with its value, how it was chosen and the values tried, and a list of what the model does not record.
• write_text() writes a writeup(), review() or ai_review() as Markdown.
• Stratified resampling (setup_KFold(), setup_StratSub(), setup_StratBoot()) stratifies a categorical variable by its levels; with more than four levels it grouped adjacent levels together.
• Metric labels print AUC, PPV and NPV in capitals.
• LightRF predictions are no longer pulled toward 0 when some trees cannot split, as happens on small samples: such trees now predict their sample's center. Training reports how many trees could not split.
• setup_SuperConfig() and setup_SuperConfigLive() reject a decomposition that cannot be applied to new data, as reading a supervised config already did.
• Shared results identify implementation-specific learner, execution, preprocessing, and resampler configs by schema and read their typed settings without inserting omitted defaults.
• write_config() and write_record() write numbers with 15 significant digits instead of rounding to four decimal places.
• calibrate() takes execution_config; calibration models train serially when it is unset.
• train() and the setup_*() functions spend less time reading property declarations and default policies; a single CART fit on iris takes about a third of the time.
• Each parallel task receives only its own resample or bag; LINADForest and bias_variance() pass training data to local workers through shared memory.
• GAM classification trains on a data.table, which failed for every outcome, and on a multiclass outcome, using mgcv's multinomial family. A multiclass GAM reports no variable importance or standard errors, and explain() refuses its GAMTerms estimator; explanation_methods() reports GAM's exactness as dependent on the fit.
• LinearSVM and RadialSVM probabilities are those of the positive class for a binary outcome and follow the outcome's level order for a multiclass one. Binary probabilities were those of the negative class when the first training case belonged to the positive class.
• setup_GridSearch() with maximize unset maximizes every metric for which a higher value is better (accuracy, balanced accuracy, sensitivity, specificity, PPV, NPV, F1, AUC, R², concordance) and minimizes the others; it minimized AUC, sensitivity, specificity, PPV, NPV and F1.
• se() is exported: standard errors of the fit for GLM, GAM, BART and LINADForest models.
• get_metrics() returns the metrics a supervised model, resampled model, clustering or decomposition stores for one sample; calibrated = TRUE selects a calibrated classifier's calibrated metrics.
• $ and [[ read the properties of resampled models, resampled metrics, tuners, LightRuleFit models and bias_variance() results, with tab completion, and [[ reads any property of a decomposition, as $ does. On these objects and on models and clusterings, a name that is not a property raises an error listing the properties.
• plot_varimp() draws a SHAP explanation's mean absolute contributions.
• plot() draws a fitted supervised model's default view: true against predicted values for regression, confusion matrices for classification, and metric distributions for resampled models. present() prints the model's description and returns the same view; plot_roc() draws ROC curves.
• Model plots color each data split the same way: training teal, validation purple, test magenta. Confusion matrices shade correct predictions blue and incorrect predictions orange.
• Imputation fitted on training data is applied to new data: apply_preprocessor(), and train() on validation, test and resampled test data, impute with what was learned from the training data rather than from the new data.
• Mean and mode imputation (impute_type = "meanMode") stores the value imputed into every feature in the new config properties impute_numeric_values and impute_level_values.
• Mean and mode imputation keeps each feature's type. impute_numeric ("mean" or "median") and impute_categorical ("mode") replace impute_discrete and impute_continuous. Integer features take the mean or median rounded half to even, logical features their more frequent value (FALSE on a tie), and factor and character features their mode, a tie going to the first level or the first value in code point order. Integer features took their mode, a tie going to the value appearing last. A supplied value that a feature's type cannot hold is an error.
• missRanger imputation keeps its fitted random forests in the Preprocessor (@imputer) and applies them with predict(); features with no missing training values take a mean or mode value on new data. All impute_missRanger_params reach missRanger, which received only pmm.k.
• micePMM imputation is available to preprocess() only: setup_SupervisedPreprocessor() does not offer it, and apply_preprocessor() refuses a preprocessor that uses it.
• setup_SupervisedPreprocessor() imputes with mean and mode values by default (impute_type = "meanMode"); missRanger, fitted on every training set of a train() run, is chosen explicitly. The Imputation sections of setup_SupervisedPreprocessor() and setup_Preprocessor() describe each method's cost and limits.

Supervised result validation

• Outcomes, predictions, and classification probabilities must have matching row counts within each sample.
• Categorical outcomes and predictions must preserve the training class levels and their order; probability matrices require one column for binary classification or one per class for multiclass classification.