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)
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Feature selection using random forest

To recap, random forest is bagging over a set of individual decision trees. Each tree considers a random subset of the features when searching for the best splitting point at each node. And, in a decision tree, only those significant features (along with their splitting values) are used to constitute tree nodes. Consider the forest as a whole: the more frequently a feature is used in a tree node, the more important it is. In other words, we can rank the importance of features based on their occurrences in nodes among all trees, and select the top most important ones.

A trained RandomForestClassifier module in scikit-learn comes with an attribute, feature_importances_, indicating the feature importance, which is calculated as the proportion of occurrences in tree nodes. Again, we will examine feature selection with random forest on the dataset with 100,000 ad click samples:

>>> from sklearn.ensemble...