Machine-Learning-3

Classification

To attempt classification, one method is to use linear regression and map all predictions greater than 0.5 as a 1 and all less than 0.5 as a 0.

However, this method doesn’t work well because classification is not actually a linear function.
The classification problem is just like the regression problem, except that the values y we now want to predict take on only a small number of discrete values. For now, we will focus on the binary classification problem in which y can take on only two values, 0 and 1. (Most of what we say here will also generalize to the multiple-class case.) For instance, if we are trying to build a spam classifier for email, then x(i) may be some features of a piece of email, and y may be 1 if it is a piece of spam mail, and 0 otherwise. Hence, y∈{0,1}. 0 is also called the negative class, and 1 the positive class, and they are sometimes also denoted by the symbols “-” and “+.” Given x(i) , the corresponding y(i) is also called the label for the training example.

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Machine Learning-1

这个系列是我跟着斯坦福的coursera中的机器学习做的笔记,本打算做个翻译来着,等翻译了一大半后发现有很多地方翻译的不是很能准确的表示含义,一怒之下就给删了...有些东西看英文原版反而更容易懂,不是吗?

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.

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