April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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學習曲線
|
127
圖
4-13
多項式回歸模型預測
還不錯
：
原始的函數是
= 0.5x
1
2
+ 1.0x
1
+ 2.0 +
高斯雜訊
，
這個模型估計
y
= 0.56
x
1
2
+ 0.93
x
1
+ 1.78。
注意
，
當特徵有多個時
，
多項式回歸或許可以找出特徵之間的關係
（
一般的線性回歸模型
沒辦法
），
因為
PolynomialFeatures
也會加入你設定的
degree
之前的每一個特徵組合
，
例
如
，
如果有兩個特徵
a
與
b，
degree=3
的
PolynomialFeatures
不但會加入特徵
a
2
、a
3
、b
2
與
b
3
，
也會加入
ab、a
2
b
與
ab
2
等組合
。
PolynomialFeatures(degree=d)
會將一個含有
n
個特徵的陣列轉換成一個
含有
(n + d)! / d!n!
個特徵的陣列
，
其中
n!
是
n
的
階乘
，
等於
1 × 2 × 3 × …
×
n
。
小心特徵組合數量爆炸
！
學習曲線
當你執行高次多項式回歸時
，
模型擬合訓練資料的程度可能優於一般的線性回歸許多
。
例
如
，
圖
4-14
用
300-degree
多項式模型來擬合上述的訓練資料
，
並且比較它和純線性模型
和二次模型
（
二次多項式
）
的結果
。
注意
300-degree
多項式模型是如何來回擺動來盡可能
地貼近訓練實例的
。
128
|
第四章：訓練模型
圖
4-14
高次多項式回歸
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.