Supervised learning：
监督学习指的就是我们给学习算法一个数据集，这个数据集由“正确答案”组成。比如房价的预测，给定一系列房子的数据，给定数据集中每个样本的正确价格，实际的售价运用学习算法，来算出更多的正确答案。用专业的术语来讲，这叫做回归问题，即试着推测出一系列连续值属性。
还有一类问题叫做分类问题，比如根据一个人的外貌预测一个人是男生还是女生，预测结果是离散的，可以根据多个特征来预测输入的变量属于哪一类型。
Supervised Learning
In supervised learning, we are given a data set and already know what our correct output
should look like, having the idea that there is a relationship between the input and the output.
Supervised learning problems are categorized into "regression" and "classification" problems.
In a regression problem, we are trying to predict results within a continuous output, meaning
that we are trying to map input variables to some continuous function. In a classification
problem, we are instead trying to predict results in a discrete output. In other words, we are
trying to map input variables into discrete categories.
Example 1:
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.
We could turn this example into a classification problem by instead making our output about
whether the house "sells for more or less than the asking price." Here we are classifying the
houses based on price into two discrete categories.
Example 2:
(a) Regression - Given a picture of a person, we have to predict their age on the basis of the
given picture
(b) Classification - Given a patient with a tumor, we have to predict whether the tumor is
malignant or benign.