通过代码”from sklearn.cluster import KMeans”引入Kmenas模块后,生成模型对象“kmeans = KMeans(n_clusters=3)”并完成对数据X完成聚类后,以下哪个代码可以查看每个样本所属簇的标签()。 A. kmeans.output_ B. kmeans.y_ C. kmeans.targets_ D. kmeans. labels_ 相关知识点: 试题来源: ...
这样，您的quotient变量现在是 * 一个 * 样本;这里我得到了一个不同的错误消息，可能是由于不同的...
题目 通过代码“from sklearn.cluster import KMeans”,引入Kmeans模块,生成模型对象“Kmeans=KMeans(n_clusters=2)”后,对于数据X训练时,要调用的方法是: A.kmeans.train()B.kmeans.fit()C.kmeans.train(X)D.kmeans.fit(X) 相关知识点: 试题来源: 解析 D 反馈 收藏 ...
awhere ci is the center of the ith cluster, and dist is the Euclidean distance [in which case it is better to work withstandardizedfeatures, and the clusters become circular (or spherical) in shape]. For two different runs ofk-means, with the same value of k but different starting protot...
awhere ci is the center of theith cluster, and dist is the Euclidean distance [in which case it is better to work withstandardizedfeatures, and the clusters become circular (or spherical) in shape]. For two different runs ofk-means, with the same value of k but different starting ...
awhereci is the center of theith cluster, and dist is the Euclidean distance [in which case it is better to work withstandardizedfeatures, and the clusters become circular (or spherical) in shape]. For two different runs ofk-means, with the same value of k but different starting prototypes...
awhere ci is the center of theith cluster, and dist is the Euclidean distance [in which case it is better to work withstandardizedfeatures, and the clusters become circular (or spherical) in shape]. For two different runs ofk-means, with the same value of k but different starting prototyp...