CatBoost, developed by Yandex, is a popular open-source gradient boosting library that is known for its high-performance on structured data problems. One of the key features of CatBoost is its ability to handle categorical features seamlessly. In addition to this, CatBoost also allows users to define and use custom evaluation metrics to further enhance the model’s performance.

In this blog post, we will explore how to define and use custom evaluation metrics in CatBoost using Python. We will walk through an example to showcase the process step by step.

Step 1: Install CatBoost

Before we begin, make sure you have CatBoost installed in your Python environment. If not, you can install it using pip:

pip install catboost

Step 2: Define the Custom Evaluation Metric

A custom evaluation metric in CatBoost is defined as a Python function that takes three arguments: predictions, actuals, and weight. Here is an example of how to define a custom evaluation metric:

def custom_metric(predictions, actuals, weight):
    # Perform calculation to compute the custom metric
    # Return the computed metric value
    return custom_metric_value

In this function, predictions is a 1-dimensional array containing the predicted values, actuals is a 1-dimensional array containing the actual values, and weight is a 1-dimensional array containing the weights for each data point.

The custom metric function should compute the desired evaluation metric using the provided arguments and return the computed metric value.

Step 3: Use the Custom Evaluation Metric in CatBoost

To use the custom evaluation metric in CatBoost, you need to pass it as a parameter to the eval_metric argument when training the CatBoost model. Here is an example of how to use the custom evaluation metric:

from catboost import CatBoostRegressor

# Instantiate the CatBoostRegressor with the custom evaluation metric
model = CatBoostRegressor(eval_metric=custom_metric)

# Train the model
model.fit(X_train, y_train)

In this example, we are using the CatBoostRegressor class from CatBoost to train a regression model. We pass the custom_metric function as the value for the eval_metric argument to inform CatBoost to use the custom evaluation metric during training.

Conclusion

In this blog post, we have learned how to define and use custom evaluation metrics in CatBoost using Python. Custom evaluation metrics provide us the flexibility to evaluate the model’s performance based on our specific needs. By defining and utilizing custom evaluation metrics, we can further enhance our models and achieve better results.

Remember to explore various evaluation metrics and choose the one that aligns best with your problem domain. Happy coding with CatBoost!