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)

Random forest - feature bagging of decision tree

The ensemble technique, bagging (which stands for bootstrap aggregating), which we briefly mentioned in the first chapter, can effectively overcome overfitting. To recap, different sets of training samples are randomly drawn with replacement from the original training data; each set is used to train an individual classification model. Results of these separate models are then combined together via majority vote to make the final decision.

Tree bagging, as previously described, reduces the high variance that a decision tree model suffers from and hence in general performs better than a single tree. However, in some cases where one or more features are strong indicators, individual trees are constructed largely based on these features and as a result become highly correlated. Aggregating multiple correlated trees will not make much difference. To force each tree to...