Hands-On Transfer Learning with Python

Classification

In the simplest terms, these algorithms help us answer objective questions or a yes-no prediction. For instance, these algorithms are useful in scenarios like is it going to rain today?, or can this tumour be cancerous?, and so on.

Formally, the key objective of classification algorithms is to predict output labels that are categorical in nature depending upon the input data points. The output labels are categorical in nature; namely, they each belong to a discrete class or category.

Logistic regression, Support Vector Machines (SVMs), Neural Networks, Random Forests, k-Nearest Neighbours (KNN), Decision Trees, and so on are some of the popular classification algorithms.

Suppose we have a real-world use case to evaluate different ...