Supervised Learning is a type of Machine Learning where the model is trained using labeled data.
This means each training example has an input and a correct output.
The model learns by comparing its predictions with the actual answers and improves over time.
Provide labeled training data (input + output)
The algorithm learns patterns from the data
The model makes predictions
Errors are corrected to improve accuracy
1. Classification
Output is a category or class
Examples:
Spam vs Not Spam
Disease detection (Yes/No)
2. Regression
Output is a continuous value
Examples:
House price prediction
Temperature forecasting
Linear Regression
Logistic Regression
Decision Tree
Random Forest
Support Vector Machine (SVM)
K-Nearest Neighbors (KNN)
Email spam detection
Face and handwriting recognition
Credit scoring
Medical diagnosis
High accuracy with sufficient labeled data
Easy to understand and implement
Requires large labeled datasets
Data labeling can be time-consuming
Supervised Learning is widely used when correct output data is available, making it one of the most reliable and popular machine learning techniques.
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