April 2020
Intermediate to advanced
344 pages
8h 22m
Japanese
Content preview from Pythonではじめる教師なし学習 ―機械学習の可能性を広げるラベルなしデータの利用
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48
2
章 機械学習プロジェクトのはじめから終わりまで
図2-5に示すように、
auROC
曲線は
0.97
となった。この図も、ロジスティック回帰モデルの良し悪
しを決める尺度になり、偽陽性率を可能な限り低く保ちながら不正をどれだけ捉えられるかを決定する
ために用いることができる。平均適合率の場合と同様に、他のモデルと比較しないと、
auROC
曲線が
0.97
であるのがどのくらい良いのかはわからない。
図2-5 ロジスティック回帰に対するauROC曲線
2.7
機械学習モデル（
Part
Ⅱ）
ロジスティック回帰が他の方法と比べてどのくらい良いのかを知るために、他の教師あり学習アルゴ
リズムを用いたモデルをいくつか作ってみよう。
2.7.1
モデル
#2
：ランダムフォレスト
まずはランダムフォレストからはじめよう。
ロジスティック回帰の場合と同様に、ハイパーパラメータを設定し、モデルを訓練し、結果を適合率
-
再現率曲線と
auROC
で評価しよう。
2.7.1.1
ハイパーパラメータの設定
n_estimators = 10
max_features = 'auto'
max_depth = None
min_samples_split = 2
min_samples_leaf = 1
min_weight_fraction_leaf = 0.0
max_leaf_nodes = None
bootstrap = True
oob_score = False
n_jobs = -1
random_state = 2018
class_weight ...
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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.
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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.