Machine Learning Models

Kinds of Supervised Machine Learning Models

📈 1. Regression Models

👉 Used when the output is a number (continuous value)

Examples:

Linear Regression

Ridge / Lasso / Elastic Net

Support Vector Regression (SVR)

Decision Tree Regressor

Random Forest Regressor

👉 Use cases:

House price prediction

Sales forecasting

📉 2. Classification Models

👉 Used when the output is a category (label)

Examples:

Logistic Regression

Support Vector Machine (SVM)

K-Nearest Neighbors (KNN)

Naive Bayes

Decision Tree Classifier

Random Forest Classifier

👉 Use cases:

Spam detection

Disease classification

🎯 3. Ensemble Models

👉 Combine multiple models to improve performance

Examples:

Random Forest

Bagging

Boosting (XGBoost, LightGBM, CatBoost)

Voting / Stacking

👉 Use cases:

High-accuracy prediction systems

Kaggle competitions

🧮 4. Probabilistic Models

👉 Based on probability distributions

Examples:

Naive Bayes

Gaussian Naive Bayes

Bayesian Regression

👉 Use cases:

Text classification

Risk prediction

📊 5. Margin-Based Models

👉 Focus on finding best boundary (margin)

Examples:

Support Vector Machine (SVM)

Support Vector Regression (SVR)

🤝 6. Instance-Based Models

👉 Learn from data points directly (lazy learning)

Examples:

K-Nearest Neighbors (KNN)

🧾 Simple way to remember

📈 Regression → numbers

📉 Classification → categories

🎯 Ensemble → combine models

🧮 Margin-based → boundary optimization

🤝 Instance-based → nearest data

💡 Most important distinction

👉 The main 2 types are:

Regression

Classification

All others are just different techniques within them

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