May 2020
Intermediate to advanced
456 pages
9h 13m
Japanese
Content preview from ゼロからはじめるデータサイエンス 第2版 ―Pythonで学ぶ基本と実践
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180
■
12
章
k
近傍法
12.2
事例：
Iris
データセット
Iris データセットは、機械学習の定番です。
3
種類のアイリス（あやめ）の測定値が
150
件含まれ、それぞれ花びらの長さ（
petal_length
）、 花 び ら の 幅 （
petal_width
）、
が く 片 の 長 さ（
sepal_length
）、 が く 片 の 幅 （
sepal_width
）、およびその品種をデー
タとして持っています。
https://archive.ics.uci.edu/ml/datasets/iris
からダウンロードで
きます。
import requests
data = requests.get(
"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
)
with open('iris.data', 'w') as f:
f.write(data.text)
データはカンマで区切られ、次のフィールドを持ちます。
sepal_length, sepal_width, petal_length, petal_width, class
最初の行を見てみましょう。
5.1,3.5,1.4,0.2,Iris-setosa
このセクションでは、
4
つの測定値からクラス（つまり品種）を予測するモデルを構
築します。
まずデータを読み込んで調べてみましょう
＊
1
。実装済みの最近傍関数は、
LabeledPoint
クラスを期待するので、デ ...
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