Fixes

• fixed problems with report() (#714)

Fixes

• fix xgboost warning (#667)

Fixes

• fix sklearn/scipy warnings (#709)
• fix report display in JupyterLab (#710)

Thanks to @lijm1358 for PR #689, it fixes problems with LightGBM tuning #645, #683.

I've added custom JSON Encoder that can handle numpy types. It fixes #496, #613, #622, #651.

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! 🎸🤖🚀

Fixes

• #637 fix problem with font loading for report

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

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/

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