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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38
2
章 機械学習プロジェクトのはじめから終わりまで
2.3.5
データの可視化
最後のステップとして、データを可視化してこのデータセットがどれほどアンバランスなのかを見て
み よ う（ 図2-2）。不正ケースが非常に少ないのでこの問題は解くのが難しい。ただ、幸いなことにデー
タセット全体に対するラベルはある。
count_classes = pd.value_counts(data['Class'],sort=True).sort_index()
ax = sns.barplot(x=count_classes.index, y=tuple(count_classes/len(data)))
ax.set_title('Frequency Percentage by Class')
ax.set_xlabel('Class')
ax.set_ylabel('Frequency Percentage')
図2-2 各クラスの割合
2.4
モデルの準備
データの準備ができたので、モデルの準備をしよう。データを訓練セットとテストセットに分割し、
コスト関数を選択し、
k
分割交差検証の準備をしなければならない。
2.4.1
訓練セットとテストセットへの分割
「1 章 機械学習エコシステムにおける教師なし学習の立ち位置」でも述べたように、機械学習アルゴ
リズムはデータから学習（すなわちデータに対して訓練）して、それまでに見たことのないデータに対
して良い性能（例えば正確な予想）が得られるようになる。見たことのないデー ...
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