Training with CatBoost
Training a model with CatBoost involves several steps and parameters that need to be configured to optimize performance. The process of feeding labeled data and configuring hyperparameters to create a CatBoost model that learns to predict target variables. Key steps include:
- Importing necessary libraries (CatBoost, NumPy, pandas, etc.)
- Loading and preprocessing your training data (handling missing values, encoding categorical features, etc.)
- Splitting data into training and validation sets.
- Defining the CatBoost model using the CatBoostClassifier or CatBoostRegressor class.
- Specifying training parameters (learning rate, number of trees, loss function, etc.).
- Training the model using the fit method on the training data.
- Evaluating the model’s performance on the validation set using metrics like accuracy, precision, recall, or AUC-ROC.
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