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scikit-learn Cookbook

scikit-learn Cookbook - Third Edition

By : John Sukup
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scikit-learn Cookbook

scikit-learn Cookbook

By: John Sukup

Overview of this book

Trusted by data scientists, ML engineers, and software developers alike, scikit-learn offers a versatile, user-friendly framework for implementing a wide range of ML algorithms, enabling the efficient development and deployment of predictive models in real-world applications. This third edition of scikit-learn Cookbook will help you master ML with real-world examples and scikit-learn 1.5 features. This updated edition takes you on a journey from understanding the fundamentals of ML and data preprocessing, through implementing advanced algorithms and techniques, to deploying and optimizing ML models in production. Along the way, you’ll explore practical, step-by-step recipes that cover everything from feature engineering and model selection to hyperparameter tuning and model evaluation, all using scikit-learn. By the end of this book, you’ll have gained the knowledge and skills needed to confidently build, evaluate, and deploy sophisticated ML models using scikit-learn, ready to tackle a wide range of data-driven challenges. *Email sign-up and proof of purchase required
Table of Contents (17 chapters)
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Regularization Theory and Practice

Regularization is a technique that is almost always utilized in real-world applications of ML so it’s worth taking a closer look at it (after all, it’s in the title of this chapter so it must be worth exploring in depth)! Regularization is an important technique used to prevent overfitting and improve the generalization of models, or, how well they can perform in nuanced datasets beyond those they were trained on.

It involves adding a penalty term to the loss function (the method we use to evaluate our model’s performance) during the training process, which discourages the model from becoming too complex or relying too heavily on specific features. By doing so, regularization helps the model to capture the underlying patterns in the data rather than memorizing noise or peculiarities of the training set.

The main idea behind regularization is to strike a balance between model complexity and goodness of fit. Without regularization...

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