What is CatBoost and Evaluation Metrics?
CatBoost, short for “Categorical Boosting,” is designed to handle categorical features without extensive preprocessing. It supports both classification and regression tasks and includes features such as handling missing values, being robust against overfitting, and efficient with GPU training.
Evaluation metrics are crucial for assessing the performance of machine learning models. Common metrics include accuracy, precision, recall, F1 score for classification tasks, and mean squared error (MSE) or mean absolute error (MAE) for regression tasks. However, standard metrics might not always align with business goals or the specific nature of the problem. This is where custom metrics come into play.
Enhancing CatBoost Model Performance with Custom Metrics
CatBoost, a machine learning library developed by Yandex, has gained popularity due to its superior performance on categorical data, fast training speed, and built-in support for various data preprocessing techniques. While CatBoost offers a range of standard evaluation metrics, leveraging custom metrics can significantly enhance the model’s performance for specific tasks.
This article explores implementing and utilizing custom metrics in CatBoost to achieve optimal model performance.
Table of Content
- What is CatBoost and Evaluation Metrics?
- Why Use Custom Metrics?
- Implementing Custom Metrics in CatBoost