catboost 0.19.1

v0.19.1

on Python PyPI

New features:

• With this release we support Text features for classification on GPU. To specify text columns use text_features parameter. Achieve better quality by using text information of your dataset. See more in Learning CatBoost with text features
• MultiRMSE loss function is now available on CPU. Labels for the multi regression mode should be specified in separate Label columns
• MonoForest framework for model analysis, based on our NeurIPS 2019 paper. Learn more in MonoForest tutorial
• boost_from_average is now True by default for Quantile and MAE loss functions, which improves the resulting quality

Speedups:

• Huge reduction of preprocessing time for datasets loaded from files and for datasets with many samples (> 10 million), which was a bottleneck for GPU training
• 3x speedup for small datasets