Day 1 - Data Science Interview Questions

1. Where is Confusion Matrix used? In Machine Learning, confusion matrix is one of the easiest way to summarize the performance of your algorithm. It is difficult to judge the accuracy of the model by just looking at the accuracy because of problems like unequal distributions. So, a better way to check how good your model is, to use a confusion matrix.

Classification Accuracy: This is the correct ratio of the number of correct predictions to the number of predictions made.

True Positives - Correct prediction of True events

False Positives - Incorrect predictions of true events

True Negatives - Correct predictions of false events

False Negatives - Incorrect prediction of false events.