import numpy as np
from sklearn.ensemble import BaggingClassifier, BaggingRegressor
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_breast_cancer, make_regression
from sklearn.metrics import accuracy_score, mean_squared_error
# Load Breast Most cancers Dataset for Classification
knowledge = load_breast_cancer()
df = pd.DataFrame(knowledge.knowledge, columns=knowledge.feature_names)
df[‘target’] = knowledge.goal
# Break up dataset
X = df.drop(‘goal’, axis=1)
y = df[‘target’]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# Bagging Classifier
classifier = BaggingClassifier(base_estimator=DecisionTreeClassifier(), n_estimators=50, random_state=42)
classifier.match(X_train, y_train)
predictions = classifier.predict(X_test)
# Consider Classification Mannequin
accuracy = accuracy_score(y_test, predictions)
print(f’Bagging Classifier Accuracy: {accuracy:.4f}’)
# Create artificial knowledge for Regression
X_reg, y_reg = make_regression(n_samples=1000, n_features=10, noise=0.2, random_state=0)
X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(X_reg, y_reg, test_size=0.2, random_state=0)
# Bagging Regressor
regressor = BaggingRegressor(base_estimator=DecisionTreeRegressor(), n_estimators=50, random_state=42)
regressor.match(X_train_reg, y_train_reg)
y_pred_reg = regressor.predict(X_test_reg)
# Consider Regression Mannequin
mse = mean_squared_error(y_test_reg, y_pred_reg)
print(f’Bagging Regressor MSE: {mse:.4f}’)
