Hyperparameter Tuning for Trees and Ensembles

As we’ve well-learned by now, hyperparameter tuning is essential for optimizing the performance of models and ensembles, including decision trees, random forests, and GBMs. By carefully selecting hyperparameters such as maximum tree depth, number of estimators, and learning rates, we can significantly enhance model performance and prevent overfitting. We will utilize the same tools (only the hyperparameters themselves will be specific to these model types) we used previously in scikit-learn, like grid search and cross-validation, to systematically tune our models. This recipe will show how we can apply hyperparameter optimization to tree-based models.

Getting ready

We'll demonstrate hyperparameter tuning using scikit-learn's GridSearchCV() with a gradient boosting classifier.

1. Load the libraries:

   import numpy as np
   import pandas as pd
   from sklearn.datasets import load_iris
   from sklearn.model_selection import train_test_split...