News

Version Updates

1.0.0

• Implement models: BARTMachineModel, LARSModel.
• Implement performance metrics: gini, multi-class pr_auc and roc_auc, multivariate rmse, msle, rmsle.
• Implement smooth calibration curves.
• Implement MLMetric class for performance metrics.
• Add as.data.frame method for ModelFrame.
• Add expand.model function.
• Add label slot to MLModel.
• Expand metricinfo/modelinfo support for mixed argument types.
• Rename calibration argument n to breaks.
• Rename modelmetrics function to performance.
• Rename ModelMetrics/Diff classes to Performance/Diff.
• Change MLModelTune slot resamples to performance.

0.4.0

• Implement models: AdaBagModel, AdaBoostModel, BlackBoostModel, EarthModel, FDAModel, GAMBoostModel, GLMBoostModel, MDAModel, NaiveBayesModel, PDAModel, RangerModel, RPartModel, TreeModel
• Implement user-specified performance metrics in modelmetrics function.
• Implement metrics: accuracy, brier, cindex, cross_entropy, f_score, kappa2, mae, mse, npv, ppv, pr_auc, precision, r2, recall, roc_auc, roc_index, sensitivity, specificity, weighted_kappa2.
• Add cutoff argument to confusion function.
• Add modelinfo and metricinfo functions.
• Add modelmetrics method for Resamples.
• Add ModelMetrics class with print and summary methods.
• Add response method for recipe.
• Export Calibration constructor.
• Export Confusion constructor.
• Export Lift constructor.
• Extend calibration arguments to observed and predicted responses.
• Extend confusion arguments to observed and predicted responses.
• Extend lift arguments to observed and predicted responses.
• Extend metrics and stats function arguments to accept function names.
• Extend Resamples to arguments with multiple models.
• Change CoxModel, GLMModel, and SurvRegModel constructor definitions so that model control parameters are specified directly instead of with a separate control argument/structure.
• Change predict(..., times = numeric()) function calls to survival model fits to return predicted values in the same direction as survival times.
• Change predict(..., times = numeric()) function calls to CForestModel fits to return predicted means instead of medians.
• Change tune function argument metrics to be defined in terms of a user-specified metric or metrics.
• Deprecate MLControl arguments cutoff, cutoff_index, na.rm, and summary.

0.3.0

• Implement linear models (LMModel), linear discriminant analysis (LDAModel), and quadratic discriminant analysis (QDAModel).
• Implement confusion matrices.
• Support matrix response variables.
• Support user-specified stratification variables for resampling via the strata argument of ModelFrame or the role of "case_strata" for recipe variables.
• Support user-specified case weights for model fitting via the role of "case_weight" for recipe variables.
• Provide fallback for models with undefined variable importance.
• Update the importing of prepper due to its relocation from rsample to recipes.

0.2.0

• Implement partial dependence, calibration, and lift estimation and plotting.
• Implement k-nearest neighbors model (KNNModel), stacked regression models (StackedModel), super learner models (SuperModel), and extreme gradient boosting (XGBModel).
• Implement resampling constructors for training resubstitution (TrainControl) and split training and test sets (SplitControl).
• Implement ModelFrame class for general model formula and dataset specification.
• Add multi-class Brier score to modelmetrics().
• Extend predict() to automatically preprocess recipes and to use training data as the newdata default.
• Extend tune() to lists of models.
• Extent summary() argument stats to functions.
• Fix survival probability calculations in GBMModel and GLMNetModel.
• Change MLControl argument na.rm default from FALSE to TRUE.
• Removed na.rm argument from modelmetrics().

0.1

• Initial public release