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Random Forests
Explore how Random Forests enhance Bagging by introducing randomness at each tree split, reducing correlation, and increasing diversity to build more accurate and stable prediction models.
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Bagging - Bootstrap Aggregation
Bagging (Bootstrap Aggregating) combines multiple high-variance models trained on different bootstrap samples to create a more stable, accurate, and lower-variance ensemble predictor.
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Introduction to Ensemble Methods
A beginner's guide to ensemble methods in machine learning, explaining how averaging and bootstrapping reduce variance and improve model performance.
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Decision Trees for Classification
Explains what makes a good split, how impurity is quantified using Gini, Entropy, and misclassification error, and why trees are both powerful and interpretable.
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Decision Trees - Our First Non-Linear Classifier
Learn how decision trees work for regression, including split criteria, overfitting control, and intuitive examples.