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Scikit-learn Cookbook

You're reading from   Scikit-learn Cookbook Over 80 recipes for machine learning in Python with scikit-learn

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Product type Paperback
Published in Dec 2025
Last Updated in Sep 2025
Publisher Packt
ISBN-13 9781836644453
Length 414 pages
Edition 3rd Edition
Languages
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Author (1):
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John Sukup John Sukup
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John Sukup
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Table of Contents (14) Chapters Close

1. Scikit-learn Cookbook, Third Edition: Over 80 recipes for machine learning in Python with scikit-learn
2. Chapter 1: Common Conventions and API Elements of scikit-learn FREE CHAPTER 3. Chapter 2: Pre-Model Workflow and Data Preprocessing 4. Chapter 3: Dimensionality Reduction Techniques 5. Chapter 4: Building Models with Distance Metrics and Nearest Neighbors 6. Chapter 5: Linear Models and Regularization 7. Chapter 6: Advanced Logistic Regression and Extensions 8. Chapter 7: Support Vector Machines and Kernel Methods 9. Chapter 8: Tree-Based Algorithms and Ensemble Methods 10. Chapter 9: Text Processing and Multiclass Classification 11. Chapter 10: Clustering Techniques 12. Chapter 11: Novelty and Outlier Detection 13. Chapter 12: Cross-Validation and Model Evaluation Techniques 14. Chapter 13: Deploying scikit-learn Models in Production

Introduction to Decision Trees

Decision trees are powerful and, compared to other ML techniques, intuitive models used for classification and regression tasks. They work by recursively splitting data based on feature values, creating a tree-like structure composed of nodes and branches. Each internal node represents a decision based on a feature, while leaf nodes represent the predicted outcome. Decision trees are popular due to their interpretability and effectiveness in handling both numerical and categorical data. Even though they are relatively easy to understand, they are still powerful and can often outperform more complex models, so don’t dismiss them from your ML arsenal! To get started, let’s get comfortable with the base implementation of scikit-learn’s decision trees using default values.

Getting ready

First, we need to prepare our environment and dataset.

  1. Load the libraries:

    import numpy as np
    import pandas as pd
    from sklearn.datasets import load_iris...
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