Book Image

Python Machine Learning by Example, - Third Edition

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning by Example, - Third Edition

By: Yuxi (Hayden) Liu

Overview of this book

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.
Table of Contents (17 chapters)
15
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16
Index

Ensembling decision trees – random forest

The ensemble technique of bagging (which stands for bootstrap aggregating), which I briefly mentioned in Chapter 1, Getting Started with Machine Learning and Python, can effectively overcome overfitting. To recap, different sets of training samples are randomly drawn with replacement from the original training data; each resulting set is used to fit an individual classification model. The results of these separately trained models are then combined together through a majority vote to make the final decision.

Tree bagging, as described in the preceding paragraph, 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...