v1.1.0

Hey there, MLJAR enthusiasts! 🌟 In this release, we're giving a high-five 🙌 to the latest and greatest versions of some rockstar ML packages:

• 🐼 pandas > 2.0.0
• 🚀 xgboost > 2.0.0 (#649)
• 🌳 dtreeviz > 2.2.2 (#631)
• 🌈 shap > 0.42.1

🐍 We're supporting Python with versions: 3.8, 3.9, 3.10, 3.11.

Fixes 🛠️

Alrighty, with great power (read: updates) comes great responsibility (read: fixes)! We've rolled up our sleeves to zap those pesky warnings caused by our major package glow-up:

• 🎓 Added classes_ for those classy classifiers (#654)
• 📊 Patched up a boo-boo in the calibration plot (#655)
• 🔧 Tweaked a model type warning that was acting all sassy (#638)

Keep rocking and happy coding! 🎸🤖🚀

v1.0.2

Fixes

• #637 fix problem with font loading for report

v1.0.1

Fixes

• #634 fix problem with categorical values in target and nan values for fairness metric
• #635 add tests for fairness feature
• #636 switch off shap exceptions printouts

v1.0.0

We add support for fairness aware training in our AutoML.

• For implementation details please check issue #612
• For example usage please check article https://mljar.com/blog/fairness-machine-learning/

0.11.5

Bug fixes and updates

• #595 replace boston example dataset with California housing dataset, replace mse metric with squared_error for tree based algorithms from sklearn
• #596 change the import method for dtreeviz package

0.11.4

Fixes

• #590 dynamically set font in a report, thanks @yairVanti!

0.11.3

Unpin shap version #551

0.11.2

Enhancements

• #523 Add type hints to AutoML class, thank you @DanielR59
• #519 save train&validation index to file in train/test split, thanks @filipsPL @MaciekEO

Bug fixes

• #496 fix exception in baseline mode, thanks @DanielR59 @moshe-rl
• #522 fixed requirements issue, thanks @DanielR59 @MaciekEO
• #514 remove warning, thanks @MaciekEO
• #511 disable EDA, thanks @MaciekEO

0.11.0

Bug fixes

• #463 change multiprocessing to Parallel with loky
• #462 handle large data for tree visualization in regression
• #419 remove/hide warnings
• #411 loose dependencies for numpy and scipy

0.10.4

Enhancements

• #81 add scatter plot predicted vs target in regression
• #158 add ROC curve for binary classification
• #336 add visualization for Optuna results
• #352 add support for Colab
• #374 update seaborn
• #378 set golden features number
• #379 switch off boost_on_errors step in Optuna mode
• #380 add custom cross validation strategy
• #386 add correlation heatmap
• #387 add residual plot
• #389 add feature importance heatmap
• #390 add custom eval metric
• #393 update sklearn

Bug fixes

• #308 fix error in kaggle kernel
• #353, #355, #366, #368, #376, #382, #383, #384 fixes

Docs

• #391 add info about hyperparameters optimization methods

Big thank you for help for: @ecoskian, @xuzhang5788, @xiaobo, @RafaD5, @drorhilman, @strelzoff-erdc, @muxuezi, @tresoldi THANK YOU !!!