Overfitting - Intro to Machine LearningYouTube↗  IA↗(long shot)
Education  43.6K views 46 likes 10 dislikes   23 Feb 2015
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先ほど見た分類境界線は
— 我们看到的这个例子中
ひじょ〜に複雑なものでした。
决策边界非常复杂
それではこのような例を考えてみましょう。
类似这样的情景
これを私たちは"over-fitting"と呼んでいます。
我们称之为过拟合 这是机器学习中常见的
機械学習ではよく起きる現象で、あなたも経験するでしょう。
同时也是需要特别关注的一个现象
例を使って説明しましょう。このようなデータがあったとします。
举个例子 这一组数据
線形の境界線が引けそうです。
看起来可以被一条直线分隔
×と○の境界線がこのようだったらどうでしょう。
有一个红色和蓝色之间的决策面 看起来类似这个
確かに全てのデータを正しく分類できていますが、常軌を逸しています。
红色数据被正确分类 但决策面的其他很多位置看上去非常奇怪
学習用データを適用させる際に厳密すぎると
如果仅仅是照搬原始数据对其进行分类
機械学習アルゴリズムはこのような複雑な境界線を作ります。
机器学习算法就会产生类似的复杂结果
シンプルな境界線が引けるにも関わらずです。これがover-fittingです。
而不是产生了像直线这样的简单结果 这就意味着出现了过拟合
機械学習ではこのようなover-fittingが起きることを回避せねばなりません。
在机器学习中 我们要避免过拟合
over-fittingを回避するためにできることの一つが
— 控制过拟合的方法之一就是
アルゴリズムのパラメータ調整です。
通过调整算法中的参数
SVMのパラメータについては説明をしました。
我们曾经讨论过 SVM 中的几个参数
Cやgamma、kernelなどを覚えているでしょう。
包括 C γ 以及核函数
SVMのover-fittingに影響を与えるパラメーターはどれでしょうか？
你认为哪些会影响 SVM 的过拟合可能性？
正しいと思うものを全て選んでください。
选出所有你认为正确的选项
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