[英]Are there advantages of using sklearn KMeans versus SciPy kmeans?
From the documentation of sklearn KMeans 来自sklearn KMeans的文档
class sklearn.cluster.KMeans(n_clusters=8, init='k-means++', n_init=10, max_iter=300, tol=0.0001, precompute_distances='auto', verbose=0, random_state=None, copy_x=True, n_jobs=1)
class sklearn.cluster.KMeans(n_clusters = 8,init ='k-means ++',n_init = 10,max_iter = 300,tol = 0.0001,precompute_distances ='auto',verbose = 0,random_state = None,copy_x = True,n_jobs = 1)
and SciPy kmeans 和SciPy kmeans
scipy.cluster.vq.kmeans(obs, k_or_guess, iter=20, thresh=1e-05, check_finite=True)
scipy.cluster.vq.kmeans(obs,k_or_guess,iter = 20,thresh = 1e-05,check_finite = True)
it is clear the number of parameters differ and perhaps more of them are available for sklearn. 很明显,参数的数量不同,也许更多的参数可供sklearn使用。
Have any of you tried one versus the other and would you have a preference for using one of them in a classification problem? 你们中的任何人都试过一个而不是另一个,你是否愿意在分类问题中使用其中一个?
Benchmark . 基准 。
And you will never touch the scipy one again. 你永远不会再触摸scipy了。
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