4.1 归一化互信息(Normalized Mutual Information, NMI) NMI衡量的是聚类结果与真实标签之间的信息共享程度。其值域为[0, 1],1表示完全一致。 from sklearn.metrics import normalized_mutual_info_score 计算NMI nmi_score = normalized_mutual_info_score(true_
importnumpyasnpfromsklearn.metricsimportnormalized_mutual_info_score# 导入numpy进行数值运算,导入sklearn中的nmi函数 1. 2. 3. 2. 定义函数计算互信息 首先,我们定义一个函数来计算互信息。互信息是基于概率分布的度量。 defcompute_mutual_info(labels_true,labels_pred):"""计算互信息"""contingency_matrix=...
importnumpyasnpfromsklearn.metricsimportnormalized_mutual_info_score# 假设我们有两个聚类结果true_labels=np.array([1,1,0,0,1,0])predicted_labels=np.array([1,0,0,0,1,1])# 计算归一化互信息nmi=normalized_mutual_info_score(true_labels,predicted_labels)print(f"归一化互信息 (NMI):{nmi}") ...
互信息用于衡量聚类结果与真实标签之间的互相关信息,其取值范围为[0, 1],值越大表示聚类结果越接近真实标签。 from sklearn.metrics import normalized_mutual_info_score 计算互信息 nmi = normalized_mutual_info_score(true_labels, y_kmeans) print(f'Normalized Mutual Information: {nmi}') 通过上述评估指标...
基于互信息的分数(Mutual Information-based Score)是一种用于衡量聚类算法性能的指标,它衡量的是聚类结果与真实标签之间的相似性。基于互信息的分数可以用于评估将样本点分为多个簇的聚类算法。 基于互信息的分数的取值范围为[0,1],其中值越接近1表示聚类结果越准确,值越接近0表示聚类结果与随机结果相当,值越小表示...
>>>from sklearnimportmetrics>>>labels_true=[0,0,0,1,1,1]>>>labels_pred=[0,0,1,1,2,2]>>>metrics.adjusted_rand_score(labels_true,labels_pred)0.24 . 1.2 Mutual Information based scores 互信息 Two different normalized versions of this measure are available, Normalized Mutual Information(...
print(metrics.normalized_mutual_info_score(A,B)) # 直接调用sklearn中的函数 运行结果: 0.3645617718571898 0.3646247961942429 分类: Machine Learning and Optimization 标签: NMI, Python 好文要顶 关注我 收藏该文 微信分享 Picassooo 粉丝- 57 关注- 4 会员号:3720 +加关注 0 0 升级成为会员 « 上...
from sklearn.metrics import silhouette_score, calinski_harabasz_score, davies_bouldin_score, adjusted_rand_score, normalized_mutual_info_scorefrom sklearn.datasets import make_blobs 生成模拟数据 X, y_true = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0) 使用K-means...
互信息( Mutual Information)也是用来衡量两个数据分布的吻合程度。利用基于互信息的方法来衡量聚类效果需要实际类别信息,MI与NMI取值范围为[0,1],AMI取值范围为[-1,1],它们都是值越大意味看聚类结果与真实倩况越吻合。 代码 from sklearn.metrics.cluster import entropy, mutual_info_score, normalized_mutual_in...
normalized_mutual_info_score:sklearn.metrics.normalized_mutual_info_score(labels_true, labels_pred) v_measure_score:sklearn.metrics.v_measure_score(labels_true, labels_pred) 注:后续含labels_true参数的均需真实值参与 6、分类常用算法 Adaboost分类:class sklearn.ensemble.AdaBoostClassifier(base_estimato...