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 ...
