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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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Practical Exercises with Tree-Based Models

In this final section, we will engage in practical exercises that involve building, tuning, and evaluating tree-based and ensemble models on real-world datasets. These exercises are designed to reinforce the concepts learned throughout the chapter and demonstrate how to effectively apply these models in complex machine learning scenarios. By the end of this section, we will have hands-on experience that we can leverage in our own ML projects.

Exercise 1: Building and Evaluating a Decision Tree Classifier

In this exercise, we'll build and evaluate a basic decision tree classifier.

Implementation steps:

  1. Load libraries.
  2. Load the dataset.
  3. Split the data.
  4. Create and train the classifier.
  5. Make predictions.
  6. Evaluate performance.

Exercise 2: Hyperparameter Tuning with Random Forests

We'll fine-tune a random forest classifier using grid search to find the optimal parameters.

Implementation steps:

  1. Load libraries.
  2. Load the dataset.
  3. Split...
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