Unravelling the mysteries of Artificial Intelligence

This is a follow up article on "The Importance of F1 Score" in which we understood the technical aspects of evaluating a Machine Learning model. In this article we will understand how different evaluation metrics can help us in designing solutions based on the problem statement and domain. I will like to distinguish evaluation metrics with respect to the following criteria: When there is life involved : In case of aircraft or ships, where a Machine Learning algorithm misses to detect a failure can be a costly affair, since there is life involved. Also in the case of detecting cancer, a failure to predict a true positive can lead to delay in treatment, and hence can be life threatening. In cases such as detecting failures in aircrafts, even if we get false positives is acceptable, but not able to detect true positives can be too expensive in terms of loss of life. Thus it becomes important to use Recall as a measure, as any positive detection becomes important, even...

At CereLabs, we are building various image classification systems. While building any kind of classification system one is often challenged to test the trained models. One useful measure to test such models is accuracy, which is the proportion of true results and the total number of images examined. Accuracy thus communicates the essential message of how close one comes to the correct result. In the case of an image classification system, accuracy is how accurately the trained model is able to classify the test image dataset. If we are trying to classify the image of an apple, accuracy will be the measure of how accurately the classifier is able to detect the apple in an image. Consider the following confusion matrix. True Positive (TP) Actual image contains an apple, and is correctly classified as an apple False Negative (FN) Actual image contains an apple but is not classified as an apple False Positive (FP) Actual image does not contain an apple but is class...