= Machine learning
{wiki}
<Machine learning> develops <algorithms> that use data or experience to improve performance on a specified task. The task, the experience available to the <algorithm> and the performance criterion must all be stated. A <statistical classification> system might learn from labelled <training data>; an <unsupervised learning> method instead looks for structure without those labels.<Deep Learning by Ian Goodfellow and others>[§5.1 and §§5.7–5.8]{ref}
For example, a <linear regression> model can learn coefficients predicting a numerical outcome from measured inputs. Minimizing its error on the <training data> determines a fitted model, but does not establish how well it predicts new observations. A separate <test set> estimates performance on data withheld from fitting. <Overfitting> occurs when fitting peculiarities of the <training data> impairs this <generalization>; <data leakage> can make the evaluation misleading.<Deep Learning by Ian Goodfellow and others>[§5.2, capacity, overfitting and underfitting]{ref}
<Statistical learning> supplies tools for reasoning about these prediction and uncertainty questions. A useful learned model is therefore assessed through an explicit task and evaluation, alongside the <running time> and other resources needed to train and use it.
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