Release 0.24.4

Speedup

• Major speedup asymmetric trees training time on CPU (2x speedup on Epsilon with 16 threads). We would like to recognize Intel software engineering team’s contributions to Catboost project.

New features

• From now on we are releasing Python 3.9 wheels. Related issues: #1491, #1509, #1510
• Allow boost_from_average for MultiRMSE loss. Issue #1515
• Add tag pairwise=False for sklearn compatibility. Fixes issue #1518

Bugfixes:

• Allow fstr calculation for datasets with embeddings
• Fix feature_importances_ for fstr with texts
• Virtual ensebles fix: use proper unshrinkage coefficients
• Fixed constants in RMSEWithUnceratainty loss function calculation to correspond values from original paper
• Allow shap values calculation for model with zero-weights and non-zero leaf values. Now we use sum of leaf weights on train and current dataset to guarantee non-zero weights for leafs, reachable on current dataset. Fixes issues #1512, #1284