Published September 2, 2021
| Version v0.19.0
Software
Open
rasbt/mlxtend: Version 0.19.0
Creators
- Sebastian Raschka1
- James Bourbeau2
- Maitreyee Mhasakar
- Reiichiro Nakano3
- Kota Mori
- Zach Griffith4
- JJLWHarrison
- Jakub Šmíd5
- Daniel
- Francisco J. H. Heras6
- Guillaume Poirier-Morency7
- Qiang Gu
- Colin8
- Floris Hoogenboom9
- Steve Harenberg
- Vahid MIRJALILI10
- Denis Barbier
- Marco Tiraboschi11
- hanzgs
- Florian Charlier
- Alejandro Correa Bahnsen12
- Gabriel Azevedo Ferreira
- Janpreet Singh
- João Pedro Zanlorensi Cardoso13
- Laurens Geffert14
- Oliver Tomic15
- Pablo Fernandez16
- Christos Aridas17
- Selay
- Ackerley Tng18
- 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 tomlxtend.plotting
for generating a scattered histogram. (#757 via Maitreyee Mhasaka) - The
evaluate.permutation_test
function now accepts apaired
argument to specify to support paired permutation/randomization tests. (#768) - The
StackingCVRegressor
now also supports multi-dimensional targets similar toStackingRegressor
viaStackingCVRegressor(..., multi_output=True)
. (#802 via Marco Tiraboschi)
- Updates unit tests for scikit-learn 0.24.1 compatibility. (#774)
StackingRegressor
now requires settingStackingRegressor(..., multi_output=True)
if the target is multi-dimensional; this allows for better input validation. (#802)- Removes deprecated
res
argument fromplot_decision_regions
. (#803) - Adds a
title_fontsize
parameter toplot_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': ''
inmlxtend.plotting.plot_decision_regions
's scatterplot highlights to stay compatible with Matplotlib 3.4 and newer. (#822) - Adds a
fontcolor_threshold
parameter to themlxtend.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 aValueError
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)
- Fixes a typo in the SequentialFeatureSelector documentation (#835 via João Pedro Zanlorensi Cardoso)
Files
rasbt/mlxtend-v0.19.0.zip
Files
(17.5 MB)
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Additional details
Related works
- Is supplement to
- https://github.com/rasbt/mlxtend/tree/v0.19.0 (URL)