ModelRefs / Classification Basics — Tutorial
Classification Basics — Tutorial
Learn to categorize data into distinct classes. Covers What is Classification, Common Classification Algorithms, Evaluation Metrics.
Overview
Learn to categorize data into distinct classes
Level: Intermediate. Estimated reading time: 40 minutes.
What is Classification?
Classification is a supervised learning task where the goal is to predict discrete categories or classes. Unlike regression (which predicts continuous values), classification assigns labels to inputs.
Examples: spam vs. not spam, cat vs. dog, disease present vs. absent.
Common Classification Algorithms
• Logistic Regression: Despite the name, used for classification • Decision Trees: Tree-like model of decisions • K-Nearest Neighbors (KNN): Classify based on similar examples • Neural Networks: Complex patterns through layers
Evaluation Metrics
• Accuracy: Percentage of correct predictions • Precision: Of positive predictions, how many were correct? • Recall: Of actual positives, how many did we find? • F1-Score: Balance between precision and recall
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