Published September 2, 2021 | Version v0.19.0
Software Open

rasbt/mlxtend: Version 0.19.0

  • 1. UW-Madison
  • 2. @coiled
  • 3. @openai
  • 4. @WIPACrepo
  • 5. Blindspot Solutions
  • 6. Champalimaud Research
  • 7. @PavlidisLab at Michael Smith Laboratories
  • 8. Google
  • 9. Royal Schiphol Group
  • 10. Data Scientist
  • 11. University of Milan
  • 12. Rappi
  • 13. UTFPR
  • 14. Nielsen
  • 15. Norwegian University of Life Sciences
  • 16. FANSI Motorsport
  • 17. Code4Thought
  • 18. Centre for Strategic Infocomm Technologies

Description

Version 0.19.0 (09/02/2021) New Features

  • Adds a second "balanced accuracy" interpretation ("balanced") to evaluate.accuracy_score in addition to the existing "average" option to compute the scikit-learn-style balanced accuracy. (#764)
  • Adds new scatter_hist function to mlxtend.plotting for generating a scattered histogram. (#757 via Maitreyee Mhasaka)
  • The evaluate.permutation_test function now accepts a paired argument to specify to support paired permutation/randomization tests. (#768)
  • The StackingCVRegressor now also supports multi-dimensional targets similar to StackingRegressor via StackingCVRegressor(..., multi_output=True). (#802 via Marco Tiraboschi)
Changes
  • Updates unit tests for scikit-learn 0.24.1 compatibility. (#774)
  • StackingRegressor now requires setting StackingRegressor(..., multi_output=True) if the target is multi-dimensional; this allows for better input validation. (#802)
  • Removes deprecated res argument from plot_decision_regions. (#803)
  • Adds a title_fontsize parameter to plot_learning_curves for controlling the title font size; also the plot style is now the matplotlib default. (#818)
  • Internal change using 'c': 'none' instead of 'c': '' in mlxtend.plotting.plot_decision_regions's scatterplot highlights to stay compatible with Matplotlib 3.4 and newer. (#822)
  • Adds a fontcolor_threshold parameter to the mlxtend.plotting.plot_confusion_matrix function as an additional option for determining the font color cut-off manually. (#827)
  • The frequent_patterns.association_rules now raises a ValueError if an empty frequent itemset DataFrame is passed. (#843)
  • The .632 and .632+ bootstrap method implemented in the mlxtend.evaluate.bootstrap_point632_score function now use the whole training set for the resubstitution weighting term instead of the internal training set that is a new bootstrap sample in each round. (#844)
Bug Fixes

Files

rasbt/mlxtend-v0.19.0.zip

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