机器学习中回归与分类之间的区别? [英] Difference between Regression and classification in Machine Learning?

问题描述

我是机器学习的新手.谁能告诉我机器学习中分类和回归之间的主要区别?

I am new to Machine Learning. Can anyone tell me the major difference between classification and regression in machine learning?

推荐答案

回归旨在预测连续的输出值.例如，假设您尝试根据许多输入参数来预测某个品牌的收入.回归模型从字面上看是一个可以根据某些输入潜在地输出任何收入数字的函数.它甚至可以输出从未在您的培训集中出现过的收益数字.

Regression aims to predict a continuous output value. For example, say that you are trying to predict the revenue of a certain brand as a function of many input parameters. A regression model would literally be a function which can output potentially any revenue number based on certain inputs. It could even output revenue numbers which never appeared anywhere in your training set.

分类旨在预测输入对应的类别(离散整数或类别标签).例如假设您已将销售分为低价销售和高价销售，并且您试图建立一个可以预测低价销售或高价销售的模型(二进制/两类分类).输入甚至可能与以前相同，但是输出将不同.在分类的情况下，您的模型将输出低"或高"，并且理论上每个输入将仅生成这两个响应之一.

Classification aims to predict which class (a discrete integer or categorical label) the input corresponds to. e.g. let us say that you had divided the sales into Low and High sales, and you were trying to build a model which could predict Low or High sales (binary/two-class classication). The inputs might even be the same as before, but the output would be different. In the case of classification, your model would output either "Low" or "High," and in theory every input would generate only one of these two responses.

(这个答案对于任何机器学习方法都是正确的；我的个人经验是关于随机森林和决策树).

(This answer is true for any machine learning method; my personal experience has been with random forests and decision trees).

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