Supervised Learning is a type of Machine Learning where the model is trained using labeled data — meaning each input has a correct output already known.
The goal is for the model to learn the mapping between inputs and outputs, so it can predict outputs for new, unseen data.
How Supervised Learning Works
- Collect Labeled Data
Example: Images labeled as cat or dog. - Train the Model
The algorithm analyzes patterns in the labeled examples. - Validate the Model
Test it on new data to check accuracy. - Predict New Outputs
The model now predicts labels or values on its own.
Types of Supervised Learning
1. Classification
Used when the output is a category or class.
Examples:
- Spam or Not Spam
- Disease: Positive / Negative
- Sentiment: Positive / Neutral / Negative
2. Regression
Used when the output is a continuous number.
Examples:
- Predicting house prices
- Estimating stock prices
- Predicting temperature
Common Algorithms in Supervised Learning
- Logistic Regression
- Linear Regression
- K-Nearest Neighbors (KNN)
- Support Vector Machine (SVM)
- Decision Trees
- Random Forest
- Naive Bayes
- Neural Networks
Each algorithm has different strengths depending on the dataset and task.
Real-World Applications
- Email Spam Filters
- Credit Score Prediction
- Face Recognition
- Medical Diagnosis
- Loan Approval Systems
- Speech Recognition
Supervised learning powers many intelligent systems you use every day.
Conclusion
Supervised Learning is one of the most widely used ML techniques. With its ability to classify and predict outcomes using labeled data, it forms the basis of many real-world AI systems.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/