Supervised Learning - Interactive Lesson

What is Supervised Learning?

Supervised learning is a type of machine learning where the algorithm learns from labeled data. You provide the algorithm with input-output pairs (training data), and it learns to map inputs to outputs. The two main types are Classification (predicting categories) and Regression (predicting continuous values).

Task Type

Algorithm

Data Points:0

Accuracy:-

Algorithm:-

Status:Ready

How to Use:

Click on the canvas to add labeled data points. Then click "Train Model" to see how the algorithm learns!

📊 Classification

Predicts discrete categories
Example: Spam vs Not Spam
Algorithms: KNN, Decision Trees, SVM
Output: Class label

📈 Regression

Predicts continuous values
Example: House price prediction
Algorithms: Linear, Polynomial
Output: Numerical value

The Supervised Learning Process:

Collect labeled data: Gather input-output pairs
Split data: Training set (80%) and test set (20%)
Choose algorithm: Select appropriate model
Train model: Learn patterns from training data
Evaluate: Test on unseen data
Predict: Use model on new inputs

🏥 Medical Diagnosis

Classify diseases from symptoms

📧 Email Filtering

Detect spam messages

💰 Price Prediction

Estimate real estate values