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AdaBoost Classifier from Scratch in Python
AdaBoost, short for Adaptive Boosting, is an ensemble boosting algorithm that combines multiple weak classifiers to create a strong classifier. In this article, we will explore how to implement the AdaBoost classifier from scratch in Python.
Understanding AdaBoost
AdaBoost works by training a series of weak classifiers on the same dataset. Each weak classifier focuses on the instances that were misclassified by the previous classifiers. The final prediction is a weighted sum of the predictions of all weak classifiers.
Implementing AdaBoost Classifier
Let’s start by importing the necessary libraries:
import numpy as np
from sklearn.tree import DecisionTreeClassifier
Next, we need to define a function to train the AdaBoost classifier:
def adaBoost(X, y, num_classifiers):
n_samples, _ = X.shape
weights = np.full(n_samples, (1 / n_samples))
classifiers = []
for _ in range(num_classifiers):
classifier = DecisionTreeClassifier(max_depth=1)
classifier.fit(X, y, sample_weight=weights)
predictions = classifier.predict(X)
error = np.sum(weights * (predictions != y))
alpha = 0.5 * np.log((1 - error) / error)
weights *= np.exp(-alpha * y * predictions)
weights /= np.sum(weights)
classifiers.append((alpha, classifier))
return classifiers
Conclusion
AdaBoost is a powerful ensemble boosting algorithm that can improve the performance of weak classifiers. By following the steps in this article, you can implement the AdaBoost classifier from scratch in Python and use it for your machine learning tasks.
