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Is there a parameter like "scale_pos_weight" in catboost package as we used to have in the xgboost package in python ?
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Abstract. As a new and efficient ensemble learning algorithm, XGBoost has been widely applied for its multitudinous advantages, but its classification effect in the case of data imbalance is often not ideal.
2. Boosting-Based techniques for imbalanced data. Boosting is an ensemble technique to combine weak learners to create a strong learner that can make accurate predictions. Boosting starts out with a base classifier / weak classifier that is prepared on the training data.
Although the algorithm performs well in general, even on imbalanced classification datasets, it offers a way to tune the training algorithm to pay more attention to misclassification of the minority class for datasets with a skewed class distribution.
Yes, the parameter is named "class_weights", you can find it here : Documentation
You have to pass a list like [0.8, 0.2] for binary or [0.3, 0.8, 0.4, 0.6] for multiclass of 4 for example. Doesn't have to sum to 1, it's used as a multiplier.
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CatBoost also has scale_pos_weight parameter starting from version 0.6.1
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