分享兴趣，传播快乐，增长见闻，留下美好！

少年易老学难成，一寸光阴不可轻。

亲爱的您，这里是LearningYard新学苑

分享兴趣，传播快乐，

增长见闻，留下美好!

亲爱的您，这里是LearningYard新学苑。

今天，小编为大家带来：

《编程日寄 | 机器学习常用算法（20）》，

欢迎您的访问。

Share interests, spread happiness,

increase knowledge, and leave good news!

Dear you, this is the new LearningYard Academy

Today, the editor brings you

"Common Algorithms for Machine Learning (20)"

Welcome your visit!

什么是机器学习

机器学习是一门多领域交叉学科，涉及概率论、统计学、逼近论、凸分析、算法复杂度理论等多门学科。专门研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技能，重新组织已有的知识结构使之不断改善自身的性能。它是人工智能核心，是使计算机具有智能的根本途径。

Machine learning is a multidisciplinary discipline, involving probability theory, statistics,approximation theory, convex analysis, algorithm complexity theory and other disciplines. It specializes in the study of how computers simulate or realize human learning behavior to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve its own performance.It is the core of artificial intelligence and the fundamental way to make computers intelligent.

机器学习的定义

（1）机器学习是一门人工智能的科学，该领域的主要研究对象是人工智能，特别是如何在经验学习中改善具体算法的性能。

（2）机器学习是对能通过经验自动改进的计算机算法的研究。

（3）机器学习运用数据或以往的经验，以此优化计算机程序的性能标准

Definition of machine learning

(1) Machine learning is a science of artificial intelligence. The main research object of this field is artificial intelligence, especially how to improve the performance of specific algorithms in experiential learning.

(2) Machine learning is the study of computer algorithms that can be improved automatically through experience.

(3) Machine learning is the use of data or past experience in order to optimize the performance criteria of computer programs

Decision Tree（决策树）

决策树(Decision Tree）是在已知各种情况发生概率的基础上，通过构成决策树来求取净现值的期望值大于等于零的概率，评价项目风险，判断其可行性的决策分析方法，是直观运用概率分析的一种图解法。由于这种决策分支画成图形很像一棵树的枝干，故称决策树。在机器学习中，决策树是一个预测模型，他代表的是对象属性与对象值之间的一种映射关系。Entropy = 系统的凌乱程度，使用算法ID3, C4.5和C5.0生成树算法使用熵。这一度量是基于信息学理论中熵的概念。

决策树是一种树形结构，其中每个内部节点表示一个属性上的测试，每个分支代表一个测试输出，每个叶节点代表一种类别。

分类树（决策树）是一种十分常用的分类方法。它是一种监督学习，所谓监督学习就是给定一堆样本，每个样本都有一组属性和一个类别，这些类别是事先确定的，那么通过学习得到一个分类器，这个分类器能够对新出现的对象给出正确的分类。这样的机器学习就被称之为监督学习。

Decision Tree is a Decision analysis method which is based on the known probability of occurrence of various situations and uses Decision Tree to obtain the probability that the expected value of net present value is greater than or equal to zero, evaluate the project risk and judge its feasibility. It is a graphical method that intuitively uses probability analysis. Because this kind of decision branch is graphically drawn like the branch of a tree, it is called a decision tree. In machine learning, decision tree is a prediction model, which represents a mapping relationship between object attributes and object values. Entropy = How messy the system is, using algorithms ID3, C4.5 and C5.0 spanning tree algorithms using Entropy. This measure is based on the concept of entropy in informatics theory.

A decision tree is a tree structure in which each internal node represents a test on an attribute, each branch represents a test output, and each leaf node represents a category.

Classification tree (decision tree) is a very common classification method. It is a kind of supervised learning, the so-called supervised learning is given a bunch of samples, each sample has a set of attributes and a category, these categories are determined in advance, then through learning to get a classifier, this classifier can give the correct classification of newly emerged objects. This kind of machine learning is called supervised learning.

今天的分享就到这里了。

如果您对今天的文章有独特的想法，

欢迎给我们留言，让我们相约明天，

祝您今天过得开心快乐！

Today's sharing is here

If you have a unique idea for today's article

Welcome to leave us a message

Let's meet tomorrow

I wish you a great day today!

内容|JTY

排版|JTY

审核|Meng

本文由LearningYard新学苑原创，

部分图片、视频素材来源网络，如侵权请沟通。

参考资料：

百度图片（图片）

百度百科（文字）

有道翻译（翻译）