有监督学习中的回归问题与分类问题
What is Machine Learning?
Two definitions of Machine Learning are offered.
- Arthur Samuel described it as: “the field of study that gives computers the ability to learn without being explicitly programmed.” This is an older, informal definition.
- Tom Mitchell provides a more modern definition: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”
Example: playing checkers.
E = the experience of playing many games of checkers
T = the task of playing checkers.
P = the probability that the program will win the next game.
In general, any machine learning problem can be assigned to one of two broad classifications:
Supervised learning and Unsupervised learning.
什么是机器学习?
机器学习的两个定义
Arthur Samuel
定义机器学习为: “研究使计算机在无需特定明确的编程下自主学习的邻域.” 这是一个很老的非正式定义Tom Mitchell
提供了一个正式的定义: “一个程序被认为能从经验E中学习,解决任务 T,达到 性能度量值P,当且仅当,有了经验E后,经过P评判, 程序在处理 T 时的性能有所提升。”
例如: 国际象棋.
E = 下棋的经验
T = 下棋的任务.
P = 程序赢得下一场比赛的概率.
一般来说,任何机器学习问题都可以分为两大类:监督学习和非监督学习
有监督学习
有监督学习(Supervised learning),是一个机器学习中的方法,可以由训练资料中学到或建立一个模式( learning model),并依此模式推测新的实例。训练资料是由输入物件(通常是向量)和预期输出所组成。函数的输出可以是一个连续的值(称为回归分析),或是预测一个分类标签(称作分类)。
回归问题
这是收集到的房价与房子大小相关性的数据轴
现在假设你有750平的房子,想要预测它能卖多少钱。那么机器学习该如何帮助你呢?它会使用这些数据试图用一条线来拟合,拟合的线可能是线性关系也可能是其他函数关系。
拟合的越完美,学习算法预测的结果就越准确。而使用什么函数来拟合,就需要经验和技术。
这就是一个简单的线性回归问题,通过已知的数据集来预测一个连续的输出值。
分类问题
我们来看另一个例子,学习判断肿瘤是否为恶性的算法。
以上是肿块大小与是否为恶性肿瘤的数据图,假设一个人的肿块大小为以下位置。
想知道自己的肿瘤是否为恶性肿瘤的话,学习算法会根据已知的数据对患者患恶性肿瘤的概率进行预测。我们再拓展一下,我们可以对恶性肿瘤的各种性质数据做采集,然后根据其值判断其患恶性肿瘤的概率,甚至是判断其患的是哪种类型的肿瘤。这就是有监督学习中的分类问题。
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.
参考资料
Supervised Learning - 斯坦福大学 | Coursera
机器学习中的有监督学习,无监督学习,半监督学习
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