If one person's opinion is good, 100 people's consensus is better. Decision Trees provide the logic, and Random Forests provide the collective intelligence.
1The Logic Tree
Decision Trees split data by asking questions (e.g., 'Is income > $50k?'). They aim to maximize 'purity' at each step, ensuring that each leaf node contains points belonging primarily to one class. They are highly interpretable but prone to overfitting.
2Strength in Numbers
A Random Forest is an ensemble of many decision trees. By training each tree on a different random subset of the data (Bagging) and a random subset of features, the forest as a whole becomes immune to the noise that might confuse a single tree.
3Purity Metrics
To decide where to split, trees use metrics like Gini Impurity or Entropy. These calculate the 'chaos' in a node. A node with 50/50 split of classes is 'impure' (high Gini), while a node with 100% of one class is 'pure' (Gini = 0).
