Monitoring and Evaluation
CatBoost provides various metrics and tools to monitor and evaluate the training process:
- Metrics: Common metrics include accuracy, precision, recall, F1-score, ROC-AUC, and RMSE. These metrics help in assessing the model’s performance.
- Overfitting Detector: The
early_stopping_rounds
parameter sets the overfitting detector, which stops training after a specified number of iterations since the optimal metric value was achieved. - Visualization: Tools for visualizing training parameters, feature importance, and overfitting help in understanding and optimizing the model.
CatBoost Training, Recovering and Snapshot Parameters
CatBoost means categorical boosting. It is a powerful open-source machine learning library known for its efficiency, accuracy, and ability to handle various data types. It excels in gradient boosting algorithms, making it suitable for classification, regression, and ranking tasks. This guide delves into the key concepts of CatBoost training, recovery from interruptions, and snapshot parameters for smooth training workflows.
Table of Content
- Training with CatBoost
- Recovering Training Progress in Catboost
- Example 1: Training a CatBoostClassifier with Snapshot Saving and Resuming
- Example 2: Regression with CatBoostRegressor Using Snapshot Mechanism
- Monitoring and Evaluation
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