(30th-May-2020)
Linear Regression and Classification
Linear functions provide the basis for many learning algorithms. In this section, we first cover regression - the problem of predicting a real-valued function from training examples. Then we consider the discrete case of classification.
Linear regression is the problem of fitting a linear function to a set of input-output pairs given a set of training examples, in which the input and output features are numeric.
Suppose the input features are X1,...,Xn. A linear function of these features is a function of the form
fw(X1,...,Xn) = w0+w1 ×X1 + ...+ wn ×Xn ,
where w=⟨w0,w1,...,wn⟩ is a tuple of weights. To make w0 not be a special case, we invent a new feature, X0, whose value is always 1.
Comments