Searching for a Good Decision Tree

(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.