Hands-On Image Processing with Python

Evaluating the performance of the classifier

Next, we need to evaluate the performance of the classifier on the test dataset. The following code snippet shows how to do that:

# evaluate the classifiert_accuracy = sum(test_predictions == test_labels) / float(len(test_labels))t_accuracy# 0.96909999999999996import pandas as pdimport seaborn as snfrom sklearn import metricscm = metrics.confusion_matrix(test_labels,test_predictions)df_cm = pd.DataFrame(cm, range(10), range(10))sn.set(font_scale=1.2)#for label sizesn.heatmap(df_cm, annot=True,annot_kws={"size": 16}, fmt="g") 

The following screenshot shows the confusion matrix for the classification; we can see there are a few misclassified test images and the overall accuracy of the training dataset ...