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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

Feature Extraction from Text

Feature extraction from text is central for enhancing the performance of text classification models by identifying meaningful patterns and attributes within textual data. Techniques such as n-grams, part-of-speech (POS) tagging, and named entity recognition (NER) provide structured insights into textual content, significantly improving model accuracy and interpretability. This recipe will teach you how to extract meaningful elements (or features) from a given corpus of text.

Getting ready

We'll load the essential libraries and prepare the dataset for feature extraction. Here we will use the Brown Corpus also built-in to the NLTK library. It contains 500 sources categories by genre.

  1. Load the libraries:

    import numpy as np
    from sklearn.feature_extraction.text import CountVectorizer
    from sklearn.model_selection import train_test_split
    import nltk
    from nltk.corpus import brown
    from nltk.util import ngrams as nltk_ngrams
    import matplotlib.pyplot as plt
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