【发布时间】:2020-05-18 02:54:38
【问题描述】:
我很困惑,我如何知道 Confusion Matrix 中的实际标签?我知道要传递标签,但我的主要问题是我们如何知道我必须传递标签的序列?
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test,y_pred_classes)
这会返回confusion_matrix() 函数的结果:
然后我声明标签并传递标签来绘制混淆矩阵:
import itertools
def plotConfusionMatrix(cm, classes, normalize=False, title='Confusion Matrix', cmap = plt.cm.Blues):
plt.figure(figsize = (10,7))
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print('Normalized Confusion Matrix')
else:
print('Un-normalized Confusion Matrix')
print(cm)
thresh = cm.max()/2
for i,j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j,i, cm[i,j], horizontalalignment='center', color='white' if cm[i,j] > thresh else 'black', fontsize=25, fontweight='bold')
plt.tight_layout()
plt.ylabel('Actual Class')
plt.xlabel('Predicted Class')
然后调用函数并传递标签:
classes = ['climbingdown','climbingup','jumping','lying','running','sitting','standing','walking']
plotConfusionMatrix(cm, classes)
绘制的混淆矩阵的输出是:
现在,我的确切问题是,我已经通过了每个班级的标签,但我将如何知道我必须通过的顺序?
【问题讨论】:
标签: python machine-learning deep-learning confusion-matrix