Multi-Label Classification 首先分清一下multiclass和multilabel:多类分类(Multiclass classification): 表示分类任务中有多个类别, 且假设每个样本都被设置了一个且仅有一个标签。比如从100个分类中击中一个。多标签分类(Multilabel classification): 给每个样本一系列的目标标签,即表示的是样本各属性而不是 多标签图...
>>> f1_score(y_true, y_pred, average=None) array([0.8, 0. , 0. ]) >>> y_true = [0, 0, 0, 0, 0, 0] >>> y_pred = [0, 0, 0, 0, 0, 0] >>> f1_score(y_true, y_pred, zero_division=1) 1.0... >>> # multilabel classification >>> y_true = [[0, 0, 0...
knn_clf.fit(x_train_transed,y_multilabel) result=knn_clf.predict([x_train_transed[10]]) print("knn 多标签分类器 ",result) #模型评测 #平均F1 fromsklearn.metricsimportf1_score y_train_knn_pred=cross_val_predict(knn_clf,x_train_transed,y_multilabel,cv=3) result=f1_score(y_multilab...
多标签混淆矩阵计算需要先从sklearn.metrics导入multilabel_confusion_matrix函数,参考sklearn官方文档: sklearn.metrics.multilabel_confusion_matrix 代码如下: from sklearn.metrics import multilabel_confusion_matrix import numpy as np classes = ['green', 'black', 'red', 'blue'] targetSrc = [[0,1,1...
除了上面提供的多标签模型评估方法之外,sklean中还提供了其他的模型评估方法,如 混淆矩阵(multilabel_confusion_matrix)、杰卡德相似系数(jaccrd_similarity_score)等,这里就不一样介绍了。 参考文档 sklearn model_evaluation 多标签分类中的损失函数与评价指标 Getting the accuracy for multi-label prediction in scikit...
‘f1’metrics.f1_scorefor binary targets(用于二进制目标) ‘f1_micro’metrics.f1_scoremicro-averaged(微平均) ‘f1_macro’metrics.f1_scoremacro-averaged(宏平均) ‘f1_weighted’metrics.f1_scoreweighted average(加权平均) ‘f1_samples’metrics.f1_scoreby multilabel sample(通过 multilabel 样本) ...
weighted,与macro类似,也是先计算各个类别的F1-score然后计算最终F1-score,只是这里不再是算数平均,而是基于各个类别样本数的来赋予各个类别F1-score权重计算得到最终F1-score; samples,Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from...
import numpy as npdef hamming_score(y_true, y_pred, normalize=True, sample_weight=None):'''Compute the Hamming score (a.k.a. label-based accuracy) for the multi-label casehttp://stackoverflow.com/q/32239577/395857'''acc_list = []for i in range(y_true.shape[0]):set_true = set...
例如一副照片中, 有多个物体, 需要给标注多个标签, 选用模型的时候,也需要模型支持多标签 mulitilabel(也是多输出 multiouput)。 A multi-label classification for an image deals with a situation where an image can belong to more than one class. For example the below image has a train, woman, girl...
再建立多标签 y_train_multilabel,代码如下 (OneVsRestClassifier 也可以用来做多标签分类): from sklearn.multiclass import OneVsRestClassifiery_train_multilabel = np.c_[ y_train%2==0, y_train<=4 ]print(y_train_multilabel) [[ True True] [False False] ...