Problem_28: Implement Bagging from Scratch using decision trees #ai #coding #ml #google

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ABOUT THIS PROBLEM:

Problem: Implement a Bagging ensemble method from scratch using decision trees as base learners. Apply this ensemble to a classification problem and evaluate its performance.

Hint: Bagging involves training multiple models on different subsets of the data and averaging their predictions.
SOLUTION:
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Problem_28: Implement Bagging from Scratch using decision trees #ai #coding #ml #google

class BaggingClassifier:
def __init__(self, base_estimator=DecisionTreeClassifier(), n_estimators=10):
self.n_estimators = n_estimators

def fit(self, X, y):
for _ in range(self.n_estimators):
X_sample, y_sample = resample(X, y)

def predict(self, X):

# Example usage:
# bagging = BaggingClassifier()

#sample_input ( #datasets )

import numpy as np

# Generate a synthetic dataset
X, y = make_classification(n_samples=100, n_features=20, n_informative=15, n_redundant=5, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize and train the BaggingClassifier
bagging = BaggingClassifier()

# Make predictions

# Evaluate the model
accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy:.2f}")

# Sample output
print("Predictions:", predictions)
print("Actual labels:", y_test)

#Sample_output :

Accuracy: 0.85
Predictions: [1. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1. 0. 1. 0.]
Actual labels: [1 0 1 0 1 0 1 1 0 1 0 1 1 0 1 0 1 0 1 0]

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