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splendid 0.4.1

  • replace all instances of deprecated function purrr::invoke() with rlang::exec()
  • only show unclassified predictions in confusion matrix output if they exist
  • add variable importance methods
  • add param vi to splendid() and splendid_model() to calculate variable importance

splendid 0.4.0

  • add elastic net penalty for multinomial logistic regression
  • fix calculation of multi-class log loss metric
  • increased minimum R version to 4.1.0
  • fix warnings pertaining to deprecated functions
  • fix reordering of SVM probability matrix

splendid 0.3.1

  • use reference cell parameterization to create dummy variables for factors. The reference level (default first level) does not have a dummy variable. A factor with k levels creates k-1 dummy variables.

  • add SVM to the list of algorithms that need all numeric variables, otherwise create dummy variables

splendid 0.3.0

  • add GitHub Action workflows for R CMD check, test coverage, and pkgdown

  • calculate AUC from yardstick

  • package DMwR has been archived, so use SMOTE implementation from package performanceEstimation

  • print error message if suggested package not available

  • fix sequential_eval() for n = 1 bootstrap case

splendid 0.2.1

  • add Kappa to evaluation metrics (overall and class-specific)

  • add class-specific accuracy and G-mean metric

splendid 0.2.0

New Features

  • use a "one-vs-all" SMOTE subsampling technique: over-sample each class vs. the other classes and combine the oversampled datasets to create the "balanced" dataset

  • new multiclass metric G-mean

  • use yardstick package for most evaluation metrics

  • pass seeds to caret::trainControl() for reproducible tuning (#48)

  • add roc_plot() for plotting multi-class ROC curves

  • add custom print method for objects returned from prediction(). The output was previously not informative and too long

  • add parameter seed_samp to splendid() to allow setting random seed before subsampling

  • splendid_convert() is now defunct. Use splendid_process() for a more comprehensive data pre-processing step. The new function can convert categorical variables to dummy variables as before. Added the ability to standardize continuous variables and apply sampling techniques to deal with class imbalance. Subsampling can only occur on the training set.

  • add parameter stratify to allow stratified bootstrap sampling on training set

  • use standard convention for confusion matrices: predicted in rows, reference in columns

  • replace MLmetrics::MultiLogLoss() with ModelMetrics::mlogLoss() in logloss() since it handles the case when the truth has a category with 0 counts but is represented in the probability matrix

  • add NPV and specificity to evaluation()

Minor Changes

  • increase minimum R version to 3.6.0

  • move packages used for classification to Suggests to reduce the number of dependencies and are used conditionally

  • remove deprecated context() from tests

  • update roxygen and docs

  • internal functions deprecated and imported from new packages as needed

  • update vignette parameter descriptions

Bug Fixes

  • put the macro and micro averaged ROC curves at the end of legend in roc_plot()

  • suppress warnings in call to multiROC::multi_roc() after updates to stats:::regularize.values() in R-3.6.0 passes warn.collapsing = TRUE if there is no value for ties in stats::approx()

  • in sequential method, remove bootstrap iterations with an undefined F1-measure from average calculation

  • increase perc.over and perc.under in SMOTE subsampling to ensure the second to smallest class has > 0 cases

  • decrease minsplit in adaboost so fewer observations are needed to split a node in the rpart classifier

  • fix num_class in xgboost: number of classes should be taken from factor levels (some might be dropped from training set)

  • fix factor order in class_threshold() to take from column order of associated probability matrix

splendid 0.1.0

  • Default seed parameter value NULL does not invoke set.seed()

  • Extend random seed parameter to more algorithms

  • Extended categorical variable conversion to classification()

  • Reinstate tidy evaluation semantics after package dependencies updated

  • Improved RFE interface

  • Added AdaBoost.M1 algorithm

  • Added a NEWS.md file to track changes to the package.