Book Image

Mastering Machine Learning with scikit-learn - Second Edition

By : Gavin Hackeling
Book Image

Mastering Machine Learning with scikit-learn - Second Edition

By: Gavin Hackeling

Overview of this book

Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance. By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.
Table of Contents (22 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
9
From Decision Trees to Random Forests and Other Ensemble Methods
Index

Summary


In this chapter, we discussed the perceptron. Inspired by neurons, the perceptron is linear model for binary classification. The perceptron classifies instances by processing a linear combination of features and weights with an activation function. While a perceptron with a logistic sigmoid activation function is the same model as logistic regression, the perceptron learns its weights using an online, error-driven algorithm. The perceptron can be used effectively in some problems. Like the other linear classifiers that we have discussed, the perceptron separates the instances of positive and negative classes using a hyperplane. Some datasets are not linearly separable; that is, no possible hyperplane can classify all the instances correctly.

In the following chapters, we will discuss two models that can be used with linearly inseparable data: ANN, which creates a universal function approximator from a graph of perceptrons, and the support vector machine, which projects the data onto...