April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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83
第三章
分類
第
1
章提到
，
回歸
（
預測值
）
與分類
（
預測類別
）
是最常見的監督學習任務
。
我們在第
2
章已經探討回歸任務
、
預測房地產價格
、
使用各種演算法
，
例如線性回歸
、
決策樹和隨機
森林
（
後續章節會進一步解釋
）
了
，
接下來要把注意力放在分類系統上面
。
MNIST
本章將使用
MNIST
資料組
，
它有
70,000
張美國高中生和人口普查局員工手寫的數字圖
片
。
每一張圖片都有一個標籤
，
指出它代表的數字
。
因為有太多研究使用這個資料組了
，
所以它經常被稱為機器學習的
「hello world」：
每當有新的分類演算法被發明出來時
，
大家
都會用
MNIST
來看看它的效果如何
，
而且每一個學習機器學習的人遲早都會處理這個資
料組
。
Scikit-Learn
有許多協助下載熱門資料組的函式
，MNIST
正是它支援的其中一種資料組
。
下面的程式可取得
MNIST
資料組
1
：
>>> from sklearn.datasets import
fetch_openml
>>>
mnist = fetch_openml('mnist_784', version=1)
>>>
mnist.keys()
dict_keys(['data', 'target', 'feature_names', 'DESCR', 'details',
'categories', 'url'])
Scikit-Learn
可以載入的資料組通常有相似的目錄結構
，
包括
：
1
在預設情況下
，Scikit-Learn
會將下載的資料組放在
$HOME/scikit_learn_data
目錄內
。
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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.