Regression models are fundamental in machine learning for predicting continuous outcomes. Here's a concise guide:

Key Concepts

  • Purpose: Estimate relationships between variables (e.g., predicting house prices based on size)
  • Types:
    • Linear Regression 📈
    • Polynomial Regression 📉
    • Logistic Regression 📊
  • Use Cases:
    • Sales forecasting
    • Trend analysis
    • Risk assessment

How to Get Started

  1. Understand the data
  2. Choose a model type
  3. Train and evaluate
    • Use metrics like MSE or R² 📈
    • Validate with cross-validation 🧪

Visual Examples

regression_model_illustration

For deeper insights, check our Machine Learning Basics Tutorial.

Common Challenges

  • Overfitting 🚫
  • Multicollinearity ⚠️
  • Non-linear relationships 📈
linear_regression_graph
logistic_regression_example