Ml Decision Trees Forests — Free Machine Learning Tutorial
Learn Ml Decision Trees Forests in Machine Learning with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Ml Decision Trees Forests in Machine Learning with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
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Ml Decision Trees Forests in Machine Learning
Decision trees split data recursively on features to create a tree of decisions. At each node, the tree asks "is feature_x > threshold?" and splits data into left and right branches. Leaves are predictions (class or continuous value). Trees are interpretable—you can trace a decision path. Training uses algorithms like CART or ID3 to choose splits that minimize impurity (Gini index for classification, MSE for regression).
Trees are prone to overfitting; they memorize training data if grown too deep. Random forests combat this by training many shallow trees on random subsets of data (bootstrap samples) and random subsets of features. Predictions are averaged (regression) or majority-voted (classification) across trees. This ensemble reduces variance and improves generalization. Feature importance (mean decrease in impurity) reveals which features matter most.
Advantages: no scaling needed, handle non-linear relationships, fast prediction. Disadvantages: biased toward high-cardinality features, unstable (small data changes cause large tree changes). XGBoost and LightGBM are modern boosting variants that often outperform random forests by iteratively correcting errors.
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