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mikropml 1.6.1

mikropml 1.6.0

CRAN release: 2023-04-14

  • New functions:
  • Improved output from find_feature_importance() (#326, @kelly-sovacool).
    • Renamed the column names to feat to represent each feature or group of correlated features.
    • New column lower and upper to report the bounds of the empirical 95% confidence interval from the permutation test. See vignette('parallel') for an example of plotting feature importance with confidence intervals.
  • Minor documentation improvements (#323, #332, @kelly-sovacool).

mikropml 1.5.0

CRAN release: 2023-01-16

mikropml 1.4.0

CRAN release: 2022-10-16

mikropml 1.3.0

CRAN release: 2022-05-20

  • mikropml now requires R version 4.1.0 or greater due to an update in the randomForest package (#292).
  • New function compare_models() compares the performance of two models with a permutation test (#295, @courtneyarmour).
  • Fixed a bug where cv_times did not affect the reported repeats for cross-validation (#291, @kelly-sovacool).
  • Made minor documentation improvements (#293, @kelly-sovacool)

mikropml 1.2.2

CRAN release: 2022-02-03

This minor patch fixes a test failure on platforms with no long doubles. The actual package code remains unchanged.

mikropml 1.2.1

CRAN release: 2022-01-30

  • Allow kfold >= length(groups) (#285, @kelly-sovacool).
    • When using the groups parameter, groups are kept together in cross-validation partitions when kfold <= the number of groups in the training set. Previously, an error was thrown if this condition was not met. Now, if there are not enough groups in the training set for groups to be kept together during CV, groups are allowed to be split up across CV partitions.
  • Report p-values for permutation feature importance (#288, @kelly-sovacool).

mikropml 1.2.0

CRAN release: 2021-11-10

  • New parameter cross_val added to run_ml() allows users to define their own custom cross-validation scheme (#278, @kelly-sovacool).
    • Also added a new parameter calculate_performance, which controls whether performance metrics are calculated (default: TRUE). Users may wish to skip performance calculations when training models with no cross-validation.
  • New parameter group_partitions added to run_ml() allows users to control which groups should go to which partition of the train/test split (#281, @kelly-sovacool).
  • Modified the training_frac parameter in run_ml() (#281, @kelly-sovacool).
    • By default, training_frac is a fraction between 0 and 1 that specifies how much of the dataset should be used in the training fraction of the train/test split.
    • Users can instead give training_frac a vector of indices that correspond to which rows of the dataset should go in the training fraction of the train/test split. This gives users direct control over exactly which observations are in the training fraction if desired.

mikropml 1.1.1

CRAN release: 2021-09-14

mikropml 1.1.0

CRAN release: 2021-08-10

mikropml 1.0.0

CRAN release: 2021-05-13

mikropml 0.0.2

CRAN release: 2020-12-03

  • Fixed a test failure on Solaris.
  • Fixed multiple test failures with R 3.6.2 due to stringsAsFactors behavior.
  • Made minor documentation improvements.
  • Moved rpart from Suggests to Imports for consistency with other packages used during model training.

mikropml 0.0.1

CRAN release: 2020-11-23

This is the first release version of mikropml! 🎉