Learn Data Science using Python

Another post starts with you beautiful people! Thanks for your overwhelming response on my previous post about  decision trees and random forests . Today in this post we will continue our Machine Learning journey and we will discover the confusion matrix interpretation for use in machine learning. After reading this post we will know: What the confusion matrix is and why we need to use it? How to calculate a confusion matrix? How to create a confusion matrix? A confusion matrix is a technique for summarizing the performance of a classification algorithm . Classification accuracy ( Classification accuracy is the ratio of correct predictions to total predictions made ) alone can be misleading if we have an unequal number of observations in each class or if we have more than two classes in our dataset. For a quick revision remember the following formula - error rate = (1 - (correct predictions / total predictions)) * 100 The main problem with classification acc...