13 May What is Regression in Machine Learning

Regression is a supervised learning technique used to predict a continuous numerical value (like price, salary, temperature, etc.) based on input features. Example:

• Predicting house prices based on size, location, and number of rooms.
• Estimating a person’s salary based on experience, education, and skills.

How Does Regression Work

1. Training Phase: The model learns the best-fit line (or curve) by adjusting coefficients (b0,b1,…b0​,b1​,…) to minimize error (usually Mean Squared Error – MSE).

2. Prediction Phase: Once trained, the model can predict new values using the learned coefficients.

Common Challenges in Regression

1. Overfitting → The Model performs well on training data but poorly on test data.

   Solution: Use regularization (Ridge/Lasso).

2. Underfitting → The Model is too simple to capture patterns.

   Solution: Add more features or use a complex model.

3. Multicollinearity → Features are highly correlated.

   Solution: Remove redundant features or use PCA.

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