Review machine learning algorithms: Linear regression

Logistic regression is used to classify, and linear regression is used to return.

Linear regression is the addition of the properties of the sample to the front plus the coefficients. The cost function is the sum of squared errors. Therefore, in the minimization of the cost function, you can directly derivative, so that the derivative equals 0, as follows:

Gradient descent can also be used to learn the same gradient as the logistic regression form.

Advantages of linear regression: simple calculation.

Cons: Not dealing with non-linear data.

Review machine learning algorithms: Linear regression