Random Forest Classifier Explained

A random forest classifier builds many decision trees instead of relying on one, then combines their predictions to produce a more reliable final answer.

Each tree is trained on a slightly different random subset of the data, so no two trees see the same information during training. This randomness is what gives the model its name.

When making a prediction, every tree in the forest votes on the outcome, and the class with the most votes becomes the final prediction. This voting process reduces the impact of any single tree's mistakes.

Because it relies on the combined judgment of many trees rather than one, a random forest classifier generally handles complex, real-world data better and overfits less than a single decision tree, though it needs more computing power to train.