classification

**Classification in Machine Learning** **Overview** Classification is a type of Supervised Learning where the goal is to predict the categorical class (label) of an input data point. **Types of Classification** **1. Binary Classification** Two possible classes (0 or 1). - Spam vs Not Spam. - Fraud vs Legitimate. - Positive vs Negative. - **Algorithms**: Logistic Regression, SVM. **2. Multi-Class Classification** Three or more mutually exclusive classes. - Image recognition: {Cat, Dog, Bird}. - Identifying Fruit: {Apple, Banana, Orange}. - **Constraint**: An input can belong to *only one* class. - **Output Layer**: Softmax (probabilities sum to 1). **3. Multi-Label Classification** An input can belong to multiple classes simultaneously. - Movie Tags: {Action, Sci-Fi, Thriller}. - News Article: {Politics, Economy}. - **Output Layer**: Sigmoid (independent probabilities per class). **Evaluation Metrics** - **Accuracy**: % Correct (Bad for imbalanced data). - **Precision**: How many predicted positives were actual positives? (Low False Positives). - **Recall**: How many actual positives did we catch? (Low False Negatives). - **F1-Score**: Harmonic mean of Precision and Recall. - **Confusion Matrix**: A table showing True methods vs Predicted values. Classification is the workhorse of enterprise AI.