What is AdaBoost in Python machine learning?

What is AdaBoost in Python machine learning?

AdaBoost, or Adaptive Boosting, is an ensemble learning technique that combines multiple weak classifiers to create a strong classifier.

How does AdaBoost improve the performance of models?

It focuses on the errors made by previous classifiers, giving more weight to misclassified instances, thus improving overall accuracy.

Can you show me a simple implementation of AdaBoost in Python?

Sure! You can use the sklearn library. Here's a basic example:

from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier

# Create a base classifier
base_classifier = DecisionTreeClassifier(max_depth=1)
# Create AdaBoost classifier
ada_boost = AdaBoostClassifier(base_classifier, n_estimators=50)

What are some advantages and disadvantages of using AdaBoost?

Advantages include improved accuracy and robustness to overfitting. Disadvantages are sensitivity to noisy data and outliers.