April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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非線性 SVM 分類
|
153
非線性
SVM
分類
雖然線性
SVM
分類器的效果很好
，
而且經常有令人驚艷的表現
，
但是很多資料組都很難
線性分隔
。
加入更多特徵是處理非線性的資料組的一種做法
，
例如加入多項式特徵
（
如同
第
4
章的做法
），
這種做法有時可以產生可線性分隔的資料組
。
考慮圖
5-5
的左圖
，
它是
個只有一個特徵
x
1
的簡單資料組
。
如你所見
，
這個資料組不是線性可分隔的
。
但如果你
加入第二個特徵
x
2
= (x
1
)
2
，
產生的
2D
資料組可以完美地線性分隔
。
圖
5-5
加入特徵來讓資料組可以線性分隔
為了使用
Scikit-Learn
來實現這個概念
，
我們建立一個
Pipeline
，
裡面有
PolynomialFeatures
轉換器
（
曾經在第
125
頁的
「
多項式回歸
」
介紹
），
接著使用
StandardScaler
與
LinearSVC
。
我們用
moons
資料組來測試它
，
這是一個二元分類玩具資
料組
，
資料點的形狀是兩個交插的半圓
（
見圖
5-6）。
你可以用
make_moons()
函式來產生
這個資料組
：
from sklearn.datasets import
make_moons
from sklearn.pipeline import
Pipeline
from sklearn.preprocessing import
PolynomialFeatures
X, y = make_moons(n_samples=100, noise=0.15)
polynomial_svm_clf = Pipeline([
("poly_features", ...
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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.