Voting Classifier

Overview A Voting Classifier is one of the simplest ensemble learning methods. It combines the predictions of multiple distinct models to produce a final result. The core idea is that "multiple weak learners can make a strong learner" if their errors are uncorrelated.

Types of Voting

1. Hard Voting (Majority Rule) Every model gets one vote.

• Example:
• Model A predicts "Spam".
• Model B predicts "Ham".
• Model C predicts "Spam".
• Result: "Spam" wins (2 vs 1).
• Best for: Classifiers that output discrete labels (like SVMs).

2. Soft Voting (Weighted Probabilities) Every model outputs a probability. The final prediction is the average of these probabilities.

• Example:
• Model A: 0.9 Spam.
• Model B: 0.4 Spam.
• Model C: 0.8 Spam.
• Average: (0.9 + 0.4 + 0.8) / 3 = 0.7.
• Result: Spam.
• Best for: Well-calibrated models (Logistic Regression, Random Forest). Soft voting typically outperforms hard voting because it captures the confidence of the prediction.