voting

**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.