Sklearn metrics for regression differ depending on the evaluation method. How to get similar scores

Sklearn Metrics for Regression: Understanding the Differences

Scikit-learn (sklearn) provides a rich set of metrics for evaluating regression models. However, the scores you obtain can vary depending on the evaluation method employed. This article delves into these differences and provides strategies to achieve similar scores across different methods.

1. Common Evaluation Methods in Sklearn

Sklearn offers two primary ways to evaluate regression models:

• Directly using scoring functions: This involves passing the predicted and actual values to specific scoring functions like mean_squared_error or r2_score.
• Utilizing model.score(): This method calls the score function of the fitted model, typically returning the $R^2$ score by default.

2. Discrepancies in Scores

The scores obtained from these methods might not always align perfectly, particularly when working with metrics like $R^2$. Here’s why:

• Default behavior of model.score(): Many regression models in sklearn default to returning the $R^2$ score, while other metrics require explicit calls to the scoring functions.
• Metric-specific considerations: Certain metrics like $R^2$ might have different interpretations based on the model type and data characteristics.

3. Achieving Consistent Scores

To obtain similar scores across different evaluation methods, follow these guidelines:

• Specify the scoring metric: When using model.score(), explicitly define the desired metric using the scoring parameter. For instance:

from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error model = LinearRegression() model.fit(X_train, y_train) # Using model.score() with specified metric r2_score = model.score(X_test, y_test) # Directly using the scoring function mse = mean_squared_error(y_test, model.predict(X_test)) 

• Ensure consistency: Use the same metric for both direct scoring and model.score() to maintain comparable results.

4. Example: Comparing $R^2$ Scores

Let’s illustrate the difference and consistency with a code example:

0.8234

0.8234

In this scenario, using model.score() with the default $R^2$ and directly calling r2_score produces identical results.

5. Conclusion

Understanding the nuances of sklearn’s regression evaluation methods is crucial for consistent and reliable model assessment. By employing explicit scoring specifications and aligning metrics across different approaches, you can achieve similar scores and make informed decisions about your models.