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Linear regression

As explained in Ensemble Methods for Machine Learning

Linear regression is a model that predicts a target as a linear combination of input variables.

Linear regression predicts a continuous target by combining input features with learned weights and an intercept. Ordinary least squares chooses these parameters by minimizing mean squared error. section 7.1.1

Synthetic regression

Generate noisy data from f(x) = −2.5x + 3.2, split it into training and test sets, fit regressors, and compare mean squared and absolute errors.

How Ensemble Methods for Machine Learning covers it

How deep it goes
Defines the squared-loss objective and compares OLS, Ridge, LASSO, and Elastic Net regularization.
Examples
Uses noisy synthetic data, train-test splitting, fitted coefficients, and MSE or mean absolute error comparisons.
What you can do after
Apply linear regression through scikit-learn and evaluate predictions with MSE and mean absolute error.
How it connects
Connects linear models with mean squared error, regularization, sparsity, feature selection, and cross-validation.
Cover of Ensemble Methods for Machine Learning
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This explainer was generated by Manning's knowledge graph from the text of Ensemble Methods for Machine Learning. The section links point to the parts of the book it draws on.