Why Use Custom Metrics?
Custom metrics allow you to tailor the evaluation process to better reflect the problem’s context and business objectives. For instance, in a fraud detection system, the cost of false negatives might be significantly higher than false positives. A custom metric can weigh these errors accordingly, leading to a model that better aligns with real-world implications.
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
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