Ensemble Learning

Ensemble learning is a machine learning technique that combines predictions from multiple individual models to create a stronger, more accurate, and robust model. The basic idea behind ensemble methods is to leverage the wisdom of the crowd; by aggregating predictions from diverse models, the ensemble can often outperform any individual model.

Why Do We Need Ensemble Learning

Increased Accuracy: Combining predictions from multiple models often leads to higher accuracy than any single model.
Robustness: Ensembles are more resistant to overfitting because they average out biases and errors present in individual models.
Handling Complexity: Ensembles can capture complex relationships in data that might be difficult for individual models to grasp.
Versatility: Ensemble methods can be applied to various types of machine learning algorithms, making them versatile and widely applicable.

Example

Let's use the famous Breast Cancer dataset from scikit-learn for this ensemble classification example. Here's how you can perform ensemble learning using Random Forest on the Breast Cancer dataset and visualize the decision boundaries:


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import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Load the Breast Cancer dataset
data = load_breast_cancer()
X, y = data.data[:, :2], data.target  # We use the first two features for visualization

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create Random Forest Classifier
rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)

# Train the classifier
rf_classifier.fit(X_train, y_train)

# Make predictions
predictions = rf_classifier.predict(X_test)

# Calculate accuracy
accuracy = accuracy_score(y_test, predictions)
print("Accuracy:", accuracy)

# Visualization
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1), np.arange(y_min, y_max, 0.1))
Z = rf_classifier.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

plt.contourf(xx, yy, Z, alpha=0.8)
plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', marker='o', s=70, linewidth=1, cmap=plt.cm.Paired)
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.title('Random Forest Classifier Decision Boundaries (Breast Cancer Dataset)')
plt.show()

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Section 1. Chapter 6