Classification algorithms are a type of supervised learning algorithm whose purpose is to analyze data and assign unseen data points to a pre-existing category, label, or classification. Classification algorithms are a very popular subset of ML, and there are many classification algorithms to choose from.
Specifically, we discussed the simple and intuitive k-nearest-neighbor algorithm, which compares a data point to its neighbors on a graph. We discussed the excellent and very popular Naive Bayes classifier, which is a classic probability-based classifier that dominates the text classification and sentiment analysis problem spaces (though it can be used for many other types of problems). We also discussed the support vector machine, an advanced geometric classifier that works well for non-linearly-separable data. Finally, we discussed the random forest classifier, a robust...