Summary

We covered a number of powerful and extremely useful classification models in this chapter, starting with the use of OLS as a classifier, and then we observed a significant increase in performance through the use of the logistic regression classifier. We then moved on to memorizing models, such as KNN, which, while simple to fit, was able to form complex non-linear boundaries in the classification process, even with images as input information into the model. Thereafter, we discussed decision trees and the ID3 algorithm. We saw how decision trees, like KNN models, memorize the training data using rules to make predictions with quite a high degree of accuracy. Finally, we concluded our introduction to classification problems with one of the most powerful classification models – artificial neural networks. We briefly covered the basics of a feedforward neural network and also showed through an exercise how it outperformed the logistic regression model on a classification...