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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5.4
階層クラスタリング
127
99, 199, 299, 399, 499, 599, 699, 783], \
columns=['overallAccuracy'])
for cutoffNumber in [9, 49, 99, 199, 299, 399, 499, 599, 699, 783]:
kmeans = KMeans(n_clusters=n_clusters, n_init=n_init, \
max_iter=max_iter, tol=tol, random_state=random_state, \
n_jobs=n_jobs)
cutoff = cutoffNumber
kmeans.fit(X_train.loc[:,0:cutoff])
kMeans_inertia.loc[cutoff] = kmeans.inertia_
X_train_kmeansClustered = kmeans.predict(X_train.loc[:,0:cutoff])
X_train_kmeansClustered = pd.DataFrame(data=X_train_kmeansClustered, \
index=X_train.index, columns=['cluster'])
countByCluster_kMeans, countByLabel_kMeans, countMostFreq_kMeans, \
accuracyDF_kMeans, ...
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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.