Summary
In this chapter, we explored the critical steps of evaluating a model’s performance and fine-tuning its parameters to enhance accuracy and efficiency. You gained practical experience with both the scikit-learn and XGBoost Python APIs, applying key performance metrics to classification and regression models.
We delved into hyperparameter tuning, learning how to adjust parameters such as learning rate, number of trees, and regularization factors to improve model fit while avoiding overfitting or underfitting. By closely monitoring performance on both training and test datasets, you ensured that your model generalizes well beyond the initial dataset.
In the next chapter, we’ll shift focus to managing the feature engineering pipeline, a vital component in maintaining consistency and efficiency when deploying models in production environments.
