Machine Learning

Linear Regression

Fits a straight-line relationship between inputs and a continuous outcome.

Minimise the squared distance between prediction and truth and you get the line of best fit. Every more sophisticated model is, in some sense, a response to the ways this one is too simple.

It rewards careful use: check the residuals, watch for correlated inputs distorting coefficients, and remember that a coefficient describes an association within your data, not a causal lever you can pull.

Also in Machine Learning

Supervised Learning Unsupervised Learning K-Means K-Nearest Neighbours Logistic Regression PCA

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