我正在计算evaluating the cluster performance的调整后兰德指数分数。假设真实集群和预测集群如下所示。格式{i, "x"}
表示元素"x"
在ith
集群中
>>> labels_true = [{0,"a"}, {0,"b"}, {0,"c"}, {1,"d"}, {1,"e"}, {1,"f"}]
>>> labels_pred = [{0,"a"}, {0,"b"}, {1,"c"}, {1,"d"}, {2,"e"}, {2,"f"}]
>>> metrics.adjusted_rand_score(labels_true, labels_pred)
ARI得分为1.0,但似乎不应为1.0,因为预测的聚类与真实聚类不同
我想知道这是否是计算ARI分数的有效方法
您只需将标签放入ARI分数功能:
labels_true = [0, 0, 0, 1, 1, 1]
labels_pred = [0, 0, 1, 1, 2, 2]
metrics.adjusted_rand_score(labels_true, labels_pred)
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