Linear Regression Explained: Types, Equations, and Evaluation Metrics

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Linear regression is a statistical method used to predict the value of a dependent variable based on one or more independent variables. It comes in two forms: Simple Linear Regression, which uses one independent variable to predict an outcome, and Multiple Linear Regression, which uses several. Key evaluation metrics help assess how well a model performs on given data. R-squared, ranging from 0 to 1, measures the proportion of variance in the dependent variable explained by the independent variables. Root Mean Squared Error (RMSE) quantifies average prediction error, with lower values indicating a better-fitting model.

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ProgrammingDEV Community ·

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