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Python Machine Learning By Example

Python Machine Learning By Example - Fourth Edition

By : Yuxi (Hayden) Liu
4.3 (25)
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Python Machine Learning By Example

Python Machine Learning By Example

4.3 (25)
By: Yuxi (Hayden) Liu

Overview of this book

The fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts. Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine. This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.
Table of Contents (18 chapters)
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16
Other Books You May Enjoy
17
Index

Implementing a decision tree from scratch

We develop the CART tree algorithm by hand on a toy dataset as follows:

Figure 3.8: An example of ad data

To begin with, we decide on the first splitting point, the root, by trying out all possible values for each of the two features. We utilize the weighted_impurity function we just defined to calculate the weighted Gini Impurity for each possible combination, as follows:

If we partition according to whether the user interest is tech, we have the 1st, 5th, and 6th samples for one group and the remaining samples for another group. Then the classes for the first group are [1, 1, 0], and the classes for the second group are [0, 0, 0, 1]:

Gini(interest, tech) = weighted_impurity([[1, 1, 0], [0, 0, 0, 1]])
                     = 0.405

If we partition according to whether the user’s interest is fashion, we have the 2nd and 3rd samples for one group and the remaining samples for another group. Then the classes for...

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