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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

5 (1)
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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Model Selection Techniques

Once we’ve evaluated our models using cross-validation, the next step is selecting the best one for deployment. Model selection techniques help us compare different algorithms and configurations in a statistically robust way. Although we won’t cover it here because, frankly, it would be quite difficult to do so with all the different permutations of factors that exist, in the real world we’d also have a variety of business rules that would impact our decision. These are typically tied to monetary metrics around costs and savings incurred by utilizing a model versus using another technique. In this recipe, we’ll use grid search and randomized search to perform hyperparameter tuning and select the optimal model based on cross-validation scores.

Getting ready

We’ll use a classification task and compare different regularization strengths for logistic regression using both exhaustive and randomized search strategies.

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