Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples, for every example in the training data set, the correct answer is already given.

监督学习：对学习数据集中的每一个样本数据都会给出相应的“正确答案”，因此可以根据答案修正学习过程，以得到最匹配的算法。

There are two types of supervised learning problems:

Regression problem

In a regression problem, the supervised learning algorithm is trying to predict results within a continuous output, meaning that it is trying to map input variables to some continuous function.

线性回归：试图预测一个连续的输出值，学习得到的算法为一个线性函。

Example

Given data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output, so this is a regression problem.

Classification problem

In a classification problem, the supervised learning algorithm is instead trying to predict results within a discrete output, meaning that it is trying to map input variables to discrete categories.

分类问题：根据数据的特点将数据划分到指定的类型中，其学习得到的算法为一个离散函数。

Example

Classification - Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.