April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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分群法（clustering）
|
245
原始照片
10
色
8
色
6
色
4
色
2
色
圖
9-12
使用
K-Means
以及各種顏色群聚數量來做圖像分割
這個工作並不難
，
是嗎
？
接下來我們來看分群的另一種應用
：
前置作業
。
用分群法來進行前置作業
分群是很有效的降維方法
，
將它當成監督學習演算法的前置步驟更是如此
。
舉一個用分
群法來降維的例子
，
我們來處理數字資料組
，
它是個類似
MNIST
的簡單資料組
，
裡面有
1,797
張代表數字
0
到
9
的
8 × 8
灰階圖像
。
我們先載入資料組
：
from sklearn.datasets import
load_digits
X_digits, y_digits = load_digits(return_X_y=True)
接著將它拆成訓練組與測試組
：
from sklearn.model_selection import
train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_digits, y_digits)
接著擬合
Logistic
回歸模型
：
from sklearn.linear_model import
LogisticRegression
log_reg = LogisticRegression()
log_reg.fit(X_train, y_train)
246
|
第九章：無監督學習技術
用測試組來評估它的準確度
：
>>>
log_reg.score(X_test, y_test)
0.9688888888888889 ...
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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.