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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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Scaling techniques

When working with datasets, features can have vastly different scales. For instance, a feature representing age may range from 0 to 100, while another feature representing income could range from 0 to 100,000. Many ML algorithms, such as KNN and gradient descent-based methods (e.g., linear regression), are sensitive to these differences in scale. Therefore, scaling helps ensure that no single feature dominates the learning process. This recipe covers the three most commonly used scaling techniques in ML.

The following are key concepts. It is worth noting that sometimes these two terms are used interchangeably, but they are not the same and should not be implemented as such!

  • Standardization (Z-score transformation) changes the data to have a mean of 0 and a standard deviation of 1
  • Normalization changes the range of the data distribution so values fall between 0 and 1

Getting ready

We will use the previously defined iterative_imputed_df DataFrame...

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