Regression

In-depth explanation

Regression is a supervised learning task where the output is a continuous number rather than a discrete category. Linear regression fits a straight line, while more complex methods like polynomial regression, random forest regression, and neural networks can capture non-linear relationships. Evaluation metrics include MSE, RMSE, and R-squared.

Examples

House price prediction

Stock price forecasting

Age estimation

More in Machine Learning

Classification

Predicting which category or class an input belongs to from a set of predefined categories.

Cross-Validation

A technique to evaluate model performance by training and testing on different subsets of data.

Ensemble Learning

Combining multiple models to produce better predictions than any single model.

Feature

An individual measurable property or characteristic of data used as input to a machine learning model.

Feature Engineering

The process of using domain knowledge to create new features that improve model performance.

Gradient Descent

An optimization algorithm that iteratively adjusts model parameters to minimize the loss function.