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 – gradient boosted trees

Boosting, which is another ensemble technique, takes an iterative approach instead of combining multiple learners in parallel. In boosted trees, individual trees are no longer trained separately. Specifically, in gradient boosted trees (GBT) (also called gradient boosting machines), individual trees are trained in succession where a tree aims to correct the errors made by the previous tree. The following two diagrams illustrate the difference between random forest and GBT:

Random forest builds each tree independently using a different subset of the dataset, and then combines the results at the end by majority votes or averaging:

Figure 4.14: The random forest workflow

The GBT model builds one tree at a time and combines the results along the way:

Figure 4.15: The GBT workflow

We will use the XGBoost package (https://xgboost.readthedocs.io/en/latest/) to implement GBT. We first install the XGBoost...