Publisher's Synopsis
Classification algorithms are essential in machine learning as they allow us to make predictions about the class or category of an input by considering its features. These algorithms have a significant impact on multiple applications like spam filtering, sentiment analysis, image recognition, and fraud detection. If you want to expand your knowledge about classification algorithms, this book is the ideal resource for you. The book starts with an introduction to problem-solving in machine learning and subsequently focuses on classification problems. It then explores the Naïve Bayes algorithm, a probabilistic method widely used in industrial applications. The application of Bayes Theorem and underlying assumptions in developing the Naïve Bayes algorithm for classification is also covered. Moving forward, the book centers its attention on the Logistic Regression algorithm, exploring the sigmoid function and its significance in binary classification. The book also covers Decision Trees and discusses the Gini Factor, Entropy, and their use in splitting trees and generating decision leaves. The Random Forest algorithm is also thoroughly explained as a cutting-edge method for classification (and regression).
