Book Image

Python Machine Learning By Example

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning By Example

By: Yuxi (Hayden) Liu

Overview of this book

Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms – they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal.
Table of Contents (9 chapters)

Click-Through Prediction with Tree-Based Algorithms

In this chapter and the next, we will be solving one of the most important machine learning problems in digital online advertising, click-through prediction—given a user and the page they are visiting, how likely they will click on a given ad. We will be herein focusing on learning tree-based algorithms, decision tree and random forest, and utilizing them to tackle the billion dollar problem.

We will get into details for the topics mentioned:

  • Introduction to online advertising click-through
  • Two types of features, numerical and categorical
  • Decision tree classifier
  • The mechanics of decision tree
  • The construction of decision tree
  • The implementations of decision tree
  • Click-through prediction with decision tree
  • Random forest
  • The mechanics of random forest
  • Click-through prediction with random forest
  • Tuning a random forest model