混淆矩阵 混淆矩阵(Confusion Matrix),也称为误差矩阵(Error Matrix),是在机器学习、统计学和模式识别中用来评估分类模型性能的工具。它是一个矩阵,用于展示一个分类器在各个分类上的性能表现,将实际类别和预测类别进行比较,从而分析分类模型的准确性、召回率、精确率等指标。 混淆矩阵的基本结构如下: Predicted Positiv...
cm = confusion_matrix(y_true, y_pred) np.set_printoptions(precision=2) cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] print(cm_normalized) plt.figure(figsize=(12, 8), dpi=120) ind_array = np.arange(len(label)) x, y = np.meshgrid(ind_array, ind_array)...
labels : array-like of shape (n_classes), default=None. List of labels to index the matrix. This may be used to reorder or select a subset of labels. If ``None`` is given, those that appear at least once in ``y_true`` or ``y_pred`` are used in sorted order. sample_weight ...
C2 = confusion_matrix(y_true, y_pred, labels=[0, 1, 2]) # 计算百分比 C2_normal = np.round(C2/np.sum(C2, axis=1).reshape(-1, 1), 2) sns.set() sns.heatmap(C2_normal, cmap="YlGnBu", annot=True) # 设置全局字体 plt.rcParams['font.sans-serif'] = 'Times New Roman' # 设...
cm = confusion_matrix(y_true, y_pred) np.set_printoptions(precision=2) cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]printcm_normalized plt.figure(figsize=(12,8), dpi=120) ind_array = np.arange(len(labels)) ...
confusion_matrix函数解释 返回值:混淆矩阵,其第i行和第j列条目表示真实标签为第i类、预测标签为第j类的样本数。 预测 0 1 真实0 1 def confusion_matrix Found at: sklearn.metrics._classification @_deprecate_positional_args def confusion_matrix(y_true, y_pred, *, labels=None, sample_weight=None,...
cm = confusion_matrix(y_true, y_pred)print cm np.set_printoptions(precision=2)cm_normalized = cm.astype('float')/cm.sum(axis=1)[:, np.newaxis]print cm_normalized plt.figure(figsize=(12,8), dpi=120)set the fontsize of label.for label in plt.gca().xaxis.get_ticklabel...
mapfunction. Note that when visualizing the matrix usingheatmap, you also need to explicitly define the y- and x-axis labels withpyplot. Also, each column and row must be defined in the heatmap function. In this way, seaborn provides more control over the specifics of the final matrix ...
cm = confusion_matrix(y_true, y_pred)print cm np.set_printoptions(precision=2)cm_normalized = cm.astype('float')/cm.sum(axis=1)[:, np.newaxis]print cm_normalized plt.figure(figsize=(12,8), dpi=120)set the fontsize of label.for label in plt.gca().xaxis.get_ticklabel...
()xlocations=np.array(range(len(labels)))plt.xticks(xlocations,labels,rotation=90)plt.yticks(xlocations,labels)plt.ylabel('True label')plt.xlabel('Predicted label')cm=confusion_matrix(y_true,y_pred)np.set_printoptions(precision=2)cm_normalized=cm.astype('float')/cm.sum(axis=1)[:,np....