Supervised Learning 监督学习

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 回归- Given a picture of a person, we have to predict their age on the basis of the given picture

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

分为回归和分类,

在回归问题中，我们试图在连续输出中预测结果，这意味着我们正在尝试将输入变量映射到某个连续函数。在分类问题中，我们试图在离散输出中预测结果。换句话说，我们正在尝试将输入变量映射到离散类别。