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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13.6
階層
DBSCAN
による時系列クラスタリング：
ECG5000
293
preds_test = km.predict(X_test)
ars = adjusted_rand_score(data_test[:,0],preds_test)
print("Adjusted Rand Index on Test Set:", ars)
TimeSeriesKMean
の実行はユークリッド距離を用いる場合の
k-Shape
法の実行よりもさらに速い。
しかし、結果はそれほど良くない。
Adjusted Rand Index of Time Series k-Means on Training Set: 0.5063464656715959
訓練セットに対する調整ランド指標は
0.506
だ。
Adjusted Rand Index of Time Series k-Means on Test Set: 0.4864981997585834
テストセットに対する調整ランド指標は
0.486
だ。
13.6
階層
DBSCAN
による時系列クラスタリング
：
ECG5000
最後に、本書の前のほうでも見た階層DBSCANを適用して、性能を評価しよう。
HDBSCAN
をデフォルトのパラメータで実行し、結果を調整ランド指標で評価する。
# Train model and evaluate on training set
min_cluster_size = 5
min_samples = None
alpha = 1.0
cluster_selection_method ...
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