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Regressione lineare
Linear regression is a statistical method that estimates the relationship between a numeric target and one or more explanatory variables, assuming this relationship can be approximated by a straight line (or a hyperplane, when there is more than one variable). It is likely the first model anyone meets when studying machine learning: simple to interpret, fast to train, and often surprisingly effective as a starting point.
The model looks for coefficients that, multiplied by each input variable and summed, produce predictions as close as possible to the observed values. Closeness is typically measured with mean squared error, and the optimal coefficients are found by minimizing this error over the whole dataset, often via the least squares method or gradient descent. The result is a readable equation: each coefficient tells you how much the prediction changes as a single feature varies.
In modern AI, linear regression remains a go-to tool for predicting continuous values, a house price from its size, product demand from historical sales, delivery time from distance and traffic, and it serves as a baseline against which more complex models are compared. Its transparency makes it valuable in contexts where the decision must be explained, not just produced, such as consumer credit or insurance.
The term regression has a curious origin unrelated to the word's everyday meaning: it was introduced by the British scientist Francis Galton in the nineteenth century, studying children's height relative to their parents'. Galton observed that children of very tall parents tended to be less extreme, closer to the average, a phenomenon he called regression toward the mean. The name stuck to the statistical technique even as its use expanded far beyond that original study.
In Grace's professional scenarios on data analysis and business intelligence, understanding linear regression lets you instruct the AI to interpret numerical forecasts correctly instead of treating them as infallible oracles.
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