What is a random forest?
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A Decision Tree is a supervised machine learning algorithm used for classification and regression tasks. It works by splitting data into branches based on feature values, forming a tree-like structure where each internal node represents a decision on a feature, each branch represents the outcome of that decision, and each leaf node represents a final prediction.
How it works:
The algorithm chooses the best feature to split the data using metrics like Gini Impurity, Entropy (Information Gain) for classification, or Variance Reduction for regression.
The dataset is recursively split into subsets until a stopping condition is met (e.g., max depth, minimum samples per node).
The prediction is made based on the majority class (classification) or average value (regression) at the leaf.
Advantages: Easy to understand, interpretable, handles both numeric and categorical data.
Disadvantages: Prone to overfitting, especially with deep trees (can be reduced with pruning).
Example: In predicting whether to play tennis, the tree might split first on “Weather” → “Humidity” → “Wind” to decide Yes/No.
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