How to apply confusion matrix on deep learning convolution neural network to evaluate the the capability of this machine learning technique for image classification.
Confusion matrix provides a performance evaluation measurement for a recognition scenario. It is a grid of actual classification vs. predicted classification. Suppose in a box there some rectangular objects (R) and some triangular (T) objects. An object is taken out at random and the computer is asked to predict its type (class). Four situations can arise for each class :
– Actually R and predicted as R (correct classification : true positive)
– Actually not R and predicted as not R (correct classification : true negative)
– Actually not R but predicted as R (incorrect classification : false positive)
– Actually R but predicted as not R (incorrect classification : false negative)
Following measures can be calculated :
(1) Accuracy = Correct predictions / Total predictions
= (TP + TN) / (TP + TN + FP + FN)
(2) Precision = True positive / Predicted positive = TP / (TP + FP)
(3) Recall = True positive / Actual positive = TP / (TP + FN)
Different systems are compared with respect to these values to find out the better system
https://www.researchgate.net/figure/Confusion-matrix-of-the-convolutional-neural-network-is-depicted_fig4_324942766
https://www.researchgate.net/figure/Multiclass-classification-confusion-matrix-using-a-Convolutional-Neural-Network-CNN_fig3_315534048
https://www.mathworks.com/help/deeplearning/ref/plotconfusion.html
On how to compute confusion matrix for image classification problem: First follow: https://de.mathworks.com/help/stats/confusionmat.html
https://de.mathworks.com/help/deeplearning/ref/plotconfusion.html
Deep learning tool box is here: https://de.mathworks.com/help/deeplearning/ref/plotconfusion.html
Good Luck.
Confusion matrix provides a performance evaluation measurement for a recognition scenario. It is a grid of actual classification vs. predicted classification. Suppose in a box there some rectangular objects (R) and some triangular (T) objects. An object is taken out at random and the computer is asked to predict its type (class). Four situations can arise for each class :
– Actually R and predicted as R (correct classification : true positive)
– Actually not R and predicted as not R (correct classification : true negative)
– Actually not R but predicted as R (incorrect classification : false positive)
– Actually R but predicted as not R (incorrect classification : false negative)
Following measures can be calculated :
(1) Accuracy = Correct predictions / Total predictions
= (TP + TN) / (TP + TN + FP + FN)
(2) Precision = True positive / Predicted positive = TP / (TP + FP)
(3) Recall = True positive / Actual positive = TP / (TP + FN)
Different systems are compared with respect to these values to find out the better system
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