英文;
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.
下面是个人理解，在监督学习中，我们有一个数据集包括正确的输出，确信在输入和输出之间有一个关系。监督学习分为回归问题和分类问题。
分类问题 预测离散值输出 能处理无穷多的特征，将结果集进行分类
回归问题 预测连续值输出 目的是给出一系列的正确答案，例如给出房子的估价。病人肿瘤是良性还是恶性也可以用回归问题解决，分类能够给出正确的离散值判断肿瘤是良性的还是恶性的，离散值是0还是1。若肿瘤的还有其他type，我们也要预测出更多的离散值。