Is there any plan to get CatBoost supporting Apple Silicon CPU ?

Problem: model.select_features/F1 problems catboost version: 0.25.1 Operating System: Ubuntu 20.04 model.select_features does not train the best model. Catboost F1 and Sci-kit learn F1 differ signi...

MLflow 1.16.0 includes several major features and improvements:
Features:

Add mlflow.pyspark.ml.autolog() API for autologging of pyspark.ml estimators (#4228, @WeichenXu123)
Add mlflow.catboost.lo...

The bootstrap_type parameter affects the following important aspects of choosing a split for a tree when building the tree structure: Regularization To prevent overfitting, the weight of each training example is varied over steps of choosing different splits (not over scoring different candidates for one split) or different trees. Speeding up When building a new tree, CatBoost calculates a score for each of the numerous split candidates. The computation complexity of this procedure is , where: is the number of numerical features, each providing many split candidates. is the number of examples. Usually, this computation dominates over all other steps of each CatBoost iteration (see Table 1 in the paper). Hence, it seems appealing to speed up this procedure by using only a part of examples for scoring all the split candidates.