- Catboost metric accuracy. One of the key aspects of using CatBoost is understanding the various metrics it provides for evaluating the performance of regression models. Objectives and metrics Logloss. Parameter: use_weights Description Use object/group weights to calculate metrics if the specified value is true and set all weights to 1 regardless of the input data if the specified value is false. CatBoost Metrics Metrics for Classification 1. For example in this ongoing Kaggle competition, the evaluation metric is Balanced Log Loss. While CatBoost offers a range of standard evaluation metrics, leveraging custom metrics can significantly enhance the model’s performance for specific tasks. score(X, y). Jul 23, 2025 ยท What are Classification Metrics? Classification metrics are used to evaluate the performance of a classification model by comparing its predictions with the actual labels or classes. This article The default values vary from one metric to another and are listed alongside the corresponding descriptions. In this article, we will delve into the world of CatBoost regression metrics, exploring what they are . ixjn mbc phw2 pjrcb1 v8d fvrhee zsn pxgs wx6 vvb
