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Linear Regression


Linear regression is an approach to machine/statistical learning generally applied to value prediction problems. It is a form of supervised learning, wherein the training data provides the “correct” answer in addition to the data points generated by an unknown function, (f). Although in this case we were provided a 2-dimensional data set, linear regression can be used on higher-dimensional data sets. The linear regression method assumes that the unknown function f can be approximated using a polynomial linear equation of d terms (the number of features being measured plus a constant value for bias). Among machine learning algorithms, it is fairly simple, and in his CalTech lectures Dr. Abu-Mostafa calls linear regression “one-step learning.”

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