1.confusion_matrix 理论部分见https://www.cnblogs.com/cxq1126/p/12990784.html#_label2 1fromsklearn.metricsimportconfusion_matrix23#if y_true.shape=y_pred.shape=(N,)4tn, fp, fn, tp = confusion_matrix(y_true, y_pred, labels=[0, 1]).ravel()5print('sensitivity:', tp/(tp+fn))6pri...
y_true=np.array([1,1,0,1,1])y_pre=np.array([1,1,0,1,1])tn,fp,fn,tp=confusion_matrix(y_true,y_pre).ravel()(tn,fp,fn,tp)# (1, 0, 0, 4)
Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator. warnings.warn(msg, category=FutureWarning) 按照提示改为使用ConfusionMatrixDisplay.from_estimator就可以了 from sklearn.metrics import ConfusionMatrixDisplay ConfusionMatrixDisplay.from_estimator(treeclf...
函数:sklearn.metrics.confusion_matrix(y_true, y_pred, labels=None, sample_weight=None) 输入: y_true:实际的目标结果 y_pred:预测的结果 labels: 标签,对结果中的string进行排序, 顺序对应0、1、2 sample_weight:样本的权重? 输出: 一个矩阵,shape=[y中的类型数,y中的类型数] 矩阵中每个值表征分类...
使用sklearn.metrics.ConfusionMatrixDisplay,可以通过以下步骤进行可视化: 首先,需要使用分类模型对数据进行预测,并得到预测结果和真实标签。 然后,使用sklearn.metrics.confusion_matrix函数计算混淆矩阵。 最后,使用sklearn.metrics.ConfusionMatrixDisplay模块的plot函数将混淆矩阵可视化。 sklearn.metrics.ConfusionMatrixDisplay...
Given the confusion matrix finds the two or multiclass metrics such as precision, recall, F1-score, etc. Confusion matrix Function outputs: FunctionOutput Description multiclass_metrics_commonPrecision Recall Accuracy Specificity F1score multiclass_metrics_specialRecall ...
示例1: plot_confusion_matrix ▲点赞 7▼ # 需要导入模块: from sklearn import metrics [as 别名]# 或者: from sklearn.metrics importconfusion_matrix[as 别名]defplot_confusion_matrix(y_true, y_pred, size=None, normalize=False):"""plot_confusion_matrix."""cm =confusion_matrix(y_true, y_pr...
"""Compute confusion matrix to evaluate the accuracy of a classification. By definition a confusion matrix :math:`C` is such that :math:`C_{i, j}` is equal to the number of observations known to be in group :math:`i` and predicted to be in group :math:`j`. ...
defprintMeasuresOfEfficiency(yTest, y_pred):# I was having an issue where the confusion matrix would come out to [[100]] or [[40]].# As opposed to how it should be: a 2x2 matrix.# After some investigation, I came to the conclusion that the test set only contained one class!# ...
knnknn = KNeighborsClassifier(6)# Fit the classifier to the training dataknn.fit(X_train,y_train)# Predict the labels of the test data: y_predy_pred = knn.predict(X_test)# Generate the confusion matrix and classification reportprint(confusion_matrix(y_test,y_pred))print(classification_rep...