Hyperparameter Tuning for Ensembles

Welcome! Today, we'll explore "Hyperparameter Tuning for Ensembles." It might sound complex, but we'll break it down step by step.

In machine learning, models learn from data and make predictions. Ensembles combine the predictions of multiple models to improve accuracy. However, tuning these models' settings (hyperparameters) is key to getting the best performance.

By the end of this lesson, you'll understand:

• What ensemble methods are.
• How to apply GridSearch to tune hyperparameters for ensemble models, specifically using the AdaBoost algorithm with a DecisionTreeClassifier as the base estimator.

Before diving into hyperparameter tuning, let's recall what ensemble methods are.

Ensemble methods use multiple models (base estimators) to make predictions. Think of them as a team of weather forecasters. Each forecaster gives their prediction, and then you combine all their predictions to get a more accurate forecast. Using ensemble methods improve model's performance and add robustness.

One popular ensemble method is AdaBoost (Adaptive Boosting), which improves model performance by combining multiple weak classifiers. Each of them focuses on errors of the previous models.

Now, let's get hands-on by setting up our dataset. We'll use the wine dataset from Scikit-Learn. This dataset contains information about different types of wines.

We need to split our dataset into training and test sets to train our model and evaluate its performance.

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split

# Load the wine dataset
X, y = load_wine(return_X_y=True)
print(X.shape, y.shape)  # Output: (178, 13) (178,)

# Split into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
print(X_train.shape, X_test.shape)  # Output: (142, 13) (36, 13)

Now, we need to define the hyperparameters we want to tune using GridSearch. This is called the parameter grid.

For AdaBoost, we can tune:

• n_estimators: Number of boosting stages.
• learning_rate: How much each model is influenced by the errors of the previous model.
• estimator__max_depth: The depth of the tree when using a DecisionTreeClassifier.

param_grid = {
    'n_estimators': [50, 100, 200],
    'learning_rate': [0.01, 0.1, 1],
    'estimator__max_depth': [1, 3, 5]
}

This grid helps us test different combinations to find the best ones.