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简单解释机器学习的 10 个概率概念
模型几乎不可能对任何事情 100% 确定。这 10 个概率概念解释了它如何做出决策。
来源:KDnuggets概率概念简介
For a long time I treated probability as the vegetables of machine learning.在到达精彩部分之前，你先把那些无聊的东西咽下去。 Later on, I realized that probability is not just a prerequisite for machine learning but it makes much of machine learning work. But nobody tells you up front that a model is almost never sure of anything. Is this image a cat?大概。 Is this transaction fraud, or did someone just buy a lot of socks at 2am?很难说。 A language model has no clue what word you're about to type next, so it doesn't pretend to. It spreads its confidence across a bunch of options and hands you the most likely one. Once that clicked for me, a lot of the rest stopped feeling like memorization. You don't need a stats PhD for any of this. You need maybe ten ideas. Here they are, the way I wish someone had explained them to me:
1. Random Variables
Let's start with one of the most fundamental ideas in probability.
在电子邮件出现之前，您不知道它是否是垃圾邮件。 You don't know if a visitor will buy anything before they arrive.在运行模型之前，您不知道模型会输出什么。任何取决于您尚未看到的结果的值都是随机变量，在机器学习中，它基本上涵盖了所有内容：特征、标签、错误、输出。
按照惯例，随机变量通常使用大写字母（例如 X 和 Y）书写，而具体观测值则使用小写字母（例如 x 和 y）书写。
So for a spam classifier you'd write the label as:
\[
Y =
\begin{cases}
1 & \text{if the email is spam} \\
0 & \text{if it's not}
\end{cases}
\]
Y is binary. Before you read the email it could be either. The second it gets labeled, the mystery's gone and you just have a number.
In supervised learning, the usual cast is X for the input features and Y for the target, and the model is chasing this:
P(Y \mid X)
Given what I can see, how likely is each label?
P(Y = 1 \mid X = x) = 0.92
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