mlr3 0.1.6 Unreleased

  • We have published an article about mlr3 in the Journal of Open Source Software: See citation("mlr3") for the citation info.
  • New method Learner$reset().
  • New method BenchmarkResult$filter().
  • Learners returned by BenchmarkResult$learners are reset to encourage the safer alternative BenchmarkResult$score() to access trained models.
  • Fix ordering of levels in PredictionClassif$set_threshold() (triggered an assertion).

mlr3 0.1.5 2019-12-10

  • Switched from package Metrics to package mlr3measures.
  • Measures can now calculate all scores using micro or macro averaging (#400).
  • Measures can now be configured to return a customizable performance score (instead of NA) in case the score cannot be calculated.
  • Character columns are now treated differently from factor columns. In the long term, character() columns are supposed to store text.
  • Fixed a bug triggered by integer grouping variables in Task (#396).
  • benchmark_grid() now accepts instantiated resamplings under certain conditions.

mlr3 0.1.4 2019-10-28

  • Task$set_col_roles() and Task$set_row_roles() are now deprecated. Instead it is recommended for now to work with the lists Task$col_roles and Task$row_roles directly.
  • Learner$predict_newdata() now works without argument task if the learner has been fitted with Learner$train() (#375).
  • Names of column roles have been unified ("weights", "label", "stratify" and "groups" have been renamed).
  • Replaced MeasureClassifF1 with MeasureClassifFScore and fixed a bug in the F1 performance calculation (#353). Thanks to @001ben for reporting.
  • Stratification is now controlled via a task column role (was a parameter of class Resampling before).
  • Added a S3 predict() method for class Learner to increase interoperability with other packages.
  • Many objects now come with a $help() which opens the respective manual page.

mlr3 0.1.3 2019-09-18

  • It is now possible to predict and score results on the training set or on both training and test set. Learners can be instructed to predict on multiple sets by setting predict_sets (default: "test"). Measures operate on all sets specified in their field predict_sets (default: "test".

  • ResampleResult$prediction and ResampleResult$predictions() are now methods instead of fields, and allow to extract predictions for different predict sets.

  • ResampleResult$performance() has been renamed to ResampleResult$score() for consistency.

  • BenchmarkResult$performance() has been renamed to BenchmarkResult$score() for consistency.

  • Changed API for (internal) constructors accepting paradox::ParamSet(). Instead of passing the initial values separately, the initial values must now be set directly in the ParamSet.

mlr3 0.1.2 2019-08-25

  • Deprecated support of automatically creating objects from strings. Instead, mlr3 provides the following helper functions intended to ease the creation of objects stored in dictionaries: tsk(), tgen(), lrn(), rsmp(), msr().

  • BenchmarkResult now ensures that the stored ResampleResults are in a persistent order. Thus, ResampleResults can now be addressed by their position instead of their hash.

  • New field BenchmarkResult$n_resample_results.

  • New field BenchmarkResult$hashes.

  • New method Task$rename().

  • New S3 generic as_benchmark_result().

  • Renamed Generator to TaskGenerator.

  • Removed the control object mlr_control().

  • Removed ResampleResult$combine().

  • Removed BenchmarkResult$best().

mlr3 0.1.1 2019-07-25

  • Initial upload to CRAN.