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Home > Data > Machine Learning > scikit-learn Cookbook
scikit-learn Cookbook
scikit-learn Cookbook

scikit-learn Cookbook: Over 80 recipes for machine learning in Python with scikit-learn , Third Edition

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scikit-learn Cookbook

Working with Metadata: Tags and More

Scikit-learn uses metadata, such as estimator tags, to control how models behave in various contexts including cross-validation and pipeline processing, and their capabilities like supported output types. Additionally, tags can provide information about an estimator such as whether it can handle multi-output data or missing values, enabling scikit-learn to optimize workflows dynamically.

scikit-learn’s metadata captures information related to model inputs and outputs and then typically uses this information to control the flow of data between different tasks in a Pipeline. Metadata objects come in two varieties, routers and consumers, where routers move metadata to consumers and consumers use that metadata in their calculations. This is known as Metadata Routing in scikit-learn.

More on metadata routing

Metadata routing in scikit-learn is a feature that allows users to control how metadata is passed between router and consumer objects in a...

Best Practices for API Usage

Once you get a feel for the underlying scikit-learn programming paradigm, you realize how powerful it is! When working with scikit-learn’s API, following best practices ensures that your code remains clear, modular, and maintainable. This includes leveraging reusable components like pipelines, adhering to the consistent fit(), predict(), and transform() methods, and making effective use of hyperparameter tuning tools like GridSearchCV(). Keeping models and data processing steps modular allows for easy debugging and scaling of your machine learning workflows.

Here are a few additional model development best practices and key takeaways as they relate to scikit-learn functionality to keep in mind as we move forward and explore some of the concepts in this chapter in further, more granular, detail:

  • Uniform API: All estimators in scikit-learn follow the same basic pattern of fit(), transform()(for transformers), and predict() methods, making code more readable...

Summary

In this chapter, we began with a high-level overview of the scikit-learn library and some of its most important features we will explore moving forward. Keep in mind, there are many additional features we haven’t yet talked about that we may stumble upon in later chapters. When applicable, callout boxes will be provided for clarity.

In the next chapter, we will begin to build our Cookbook with recipes for one of the most important stages in ML model development: data preprocessing. Let’s get going!

Handling missing data

Missing data can arise from various sources, including human error, technical failures, or data corruption. It is important to address missing values before training ML models, as most algorithms cannot handle them directly, and most scikit-learn methods won’t even execute when they are detected in your training data. Sometimes, with large enough datasets, we can simply drop the records that contain missing values with little impact on the resulting model, but this isn’t always viable. Thankfully, scikit-learn provides several strategies for imputing missing values, allowing practitioners to fill in gaps with estimated values based on available data. This recipe introduces three of the most commonly used methods for imputing missing values in a dataset with scikit-learn.

Getting ready

To begin, we will create a toy dataset composed of random, quantitative data, 10 features, and several missing data values randomly spread throughout. We will...

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...

Encoding categorical variables

Categorical variables are a common feature in many datasets, representing discrete values such as categories, labels, or groups. However, most ML algorithms (well, computers in general, it should be said) require numerical input, making it essential to convert categorical data into a suitable format.

Categorical variables can be divided into two main types:

  • Nominal variables: These represent categories without any intrinsic ordering (e.g., color, brand)
  • Ordinal variables: These have a clear ordering among categories (e.g., ratings from 1 to 5)

Choosing the right encoding method depends on the type of categorical variable and the specific requirements of the ML algorithm being used. These recipes teach us how to convert non-numeric variables into a numeric representation that our training algorithms can utilize appropriately.

Getting ready

To begin, like we did earlier, we will create a toy dataset, only this time, our features...

Introduction to pipelines in scikit-learn

In ML, managing the workflow of data preprocessing and model training can become complex, especially when multiple steps are involved (which is almost always the case in the real world). The Pipeline() class in scikit-learn offers a powerful solution to streamline this process. By allowing users to chain together various preprocessing steps and model training into a single object, pipelines enhance code efficiency and reduce the likelihood of errors. This recipe will introduce you to the concept of pipelines, demonstrating how to create and utilize them effectively for data preprocessing in scikit-learn. We’ll be utilizing pipelines throughout the book as we add more steps to our model development workflow.

What is a pipeline?

A pipeline in scikit-learn is essentially a sequence of steps that are executed in order. Each step in the pipeline consists of a name and an associated transformer or estimator (refer to Chapter 1 if you...

Feature engineering

Feature engineering is really an umbrella term that generally refers to two main activities: feature extraction and feature selection. Effective feature engineering can significantly enhance model performance by providing algorithms with more informative inputs and reducing or removing noisy and/or uninformative ones. These recipes will teach common approaches to feature engineering using existing features to generate new features that may (“may” being the keyword) improve model performance.

Understanding feature engineering

Feature engineering encompasses two main activities:

  1. Creating new features (feature extraction): This involves transforming existing data into new variables that may capture important patterns or relationships. For example, you might derive a total spending feature by combining price and quantity features.
  2. Selecting relevant features (feature selection): This process identifies and retains the most informative...

Practical exercises on data preprocessing

In this chapter, we’ve covered several methods commonly applied to data preprocessing. Now it’s time to put it all together! Can you guess what tool might be helpful for this exercise? You got it: the Pipeline() class!

How to do it…

For these exercises, we will use a publicly available dataset, California Housing, which is included in the scikit-learn library. The dataset contains 20,640 records and 9 features, where the target value (what we are trying to predict with our model) is the average home price per 100,000 homes.

You are tasked with building a comprehensive data pipeline composed of steps you learned in this chapter. In the Jupyter notebook for Chapter 2, you will find an incomplete code block at the end called Comprehensive Pipeline, where you should add your code to complete the following steps:

  1. Load the California Housing dataset.
  2. Split the data.
  3. Create a comprehensive pipeline with...
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Key benefits

  • Solve complex business problems with data-driven approaches
  • Master tools associated with developing predictive and prescriptive models
  • Build robust ML pipelines for real-world applications, avoiding common pitfalls
  • Free with your book: PDF Copy, AI Assistant, and Next-Gen Reader

Description

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.

Who is this book for?

This book is for data scientists as well as machine learning and software development professionals looking to deepen their understanding of advanced ML techniques. To get the most out of this book, you should have proficiency in Python programming and familiarity with commonly used ML libraries; e.g., pandas, NumPy, matplotlib, and sciPy. An understanding of basic ML concepts, such as linear regression, decision trees, and model evaluation metrics will be helpful. Familiarity with mathematical concepts such as linear algebra, calculus, and probability will also be invaluable.

What you will learn

  • Implement a variety of ML algorithms, from basic classifiers to complex ensemble methods, using scikit-learn
  • Perform data preprocessing, feature engineering, and model selection to prepare datasets for optimal model performance
  • Optimize ML models through hyperparameter tuning and cross-validation techniques to improve accuracy and reliability
  • Deploy ML models for scalable, maintainable real-world applications
  • Evaluate and interpret models with advanced metrics and visualizations in scikit-learn
  • Explore comprehensive, hands-on recipes tailored to scikit-learn version 1.5

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 19, 2025
Length: 388 pages
Edition : 3rd
Language : English
ISBN-13 : 9781836644446
Category :
Data
Languages :
Python
Concepts :
Machine Learning
Tools :
Scikit-learn

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Publication date : Dec 19, 2025
Length: 388 pages
Edition : 3rd
Language : English
ISBN-13 : 9781836644446
Category :
Data
Languages :
Python
Concepts :
Machine Learning
Tools :
Scikit-learn

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Table of Contents

16 Chapters
Chapter 1: Common Conventions and API Elements of scikit-learn Chevron down icon Chevron up icon
Chapter 1: Common Conventions and API Elements of scikit-learn
Technical requirements
Introduction to scikit-learn’s design philosophy
Understanding estimators
Transformers and the transform() method
Handling custom estimators and transformers
Pipelines and workflow automation
Common attributes and methods
Hyperparameter tuning with search methods
Working with metadata: Tags and more
Best practices for API usage
Chapter 2: Pre-Model Workflow and Data Preprocessing Chevron down icon Chevron up icon
Chapter 2: Pre-Model Workflow and Data Preprocessing
Technical requirements
The impact of raw data on model performance
Handling missing data
Scaling techniques
Encoding categorical variables
Introduction to pipelines in scikit-learn
Feature engineering
Practical exercises on data preprocessing
Chapter 3: Dimensionality Reduction Techniques Chevron down icon Chevron up icon
Chapter 3: Dimensionality Reduction Techniques
Technical requirements
Introduction to dimensionality reduction
Transforming datasets with PCA
Maximizing class separability with LDA
t-SNE and data visualization
Impact on model performance
Practical exercises in dimensionality reduction
Chapter 4: Building Models with Distance Metrics and Nearest Neighbors Chevron down icon Chevron up icon
Chapter 4: Building Models with Distance Metrics and Nearest Neighbors
Technical requirements
Introduction to distance metrics
Understanding KNNs
Distance metrics overview
Hyperparameter tuning in KNN
Evaluating KNN performance
Practical exercises with KNN models
Chapter 5: Linear Models and Regularization Chevron down icon Chevron up icon
Chapter 5: Linear Models and Regularization
Technical requirements
Introduction to linear models
Ridge and Lasso regression
ElasticNet and regularization
Regularization theory and practice
Regression and regularization
Practical exercises with regularization techniques
Chapter 6: Advanced Logistic Regression and Extensions Chevron down icon Chevron up icon
Chapter 6: Advanced Logistic Regression and Extensions
Technical requirements
Overview of logistic regression
Multiclass classification techniques
Regularization in logistic regression
Multilabel classification concepts
Model evaluation metrics
Practical exercises with advanced logistic regression
Chapter 7: Support Vector Machines and Kernel Methods Chevron down icon Chevron up icon
Chapter 7: Support Vector Machines and Kernel Methods
Technical requirements
Introduction to SVMs
Kernel functions and their applications
Tuning SVM parameters
SVMs in high-dimensional spaces
Evaluating SVM models
Practical exercises with SVMs
Chapter 8: Tree-Based Algorithms and Ensemble Methods Chevron down icon Chevron up icon
Chapter 8: Tree-Based Algorithms and Ensemble Methods
Technical requirements
Introduction to decision trees
Random forests and bagging
Gradient boosting machines
Hyperparameter tuning for trees and ensembles
Comparing ensemble methods
Practical exercises with tree-based models
Chapter 9: Text Processing and Multiclass Classification Chevron down icon Chevron up icon
Chapter 9: Text Processing and Multiclass Classification
Technical requirements
Introduction to text processing
Text vectorization techniques
Feature extraction from text
Implementing text classification models
Multiclass classification strategies
Evaluating text models
Practical exercises in text processing
Chapter 10: Clustering Techniques Chevron down icon Chevron up icon
Chapter 10: Clustering Techniques
Technical requirements
Introduction to clustering
K-means clustering
Hierarchical clustering
Density-based clustering with DBSCAN
Cluster evaluation metrics
Choosing the right clustering algorithm
Advanced clustering techniques
Practical exercises with clustering models
Chapter 11: Novelty and Outlier Detection Chevron down icon Chevron up icon
Chapter 11: Novelty and Outlier Detection
Technical requirements
Introduction to outlier and novelty detection
Understanding Isolation Forest
One-Class SVM for novelty detection
Detecting outliers with LOF
Evaluating outlier detection models
Handling detected outliers
Choosing the right detection technique
Practical exercises in novelty and outlier detection
Chapter 12: Cross-Validation and Model Evaluation Techniques Chevron down icon Chevron up icon
Chapter 12: Cross-Validation and Model Evaluation Techniques
Technical requirements
Introduction to cross-validation
Advanced cross-validation methods
Implementing cross-validation in scikit-learn
Model selection techniques
Evaluating model generalizability
Practical exercises in cross-validation and evaluation
Chapter 13: Deploying scikit-learn Models in Production Chevron down icon Chevron up icon
Chapter 13: Deploying scikit-learn Models in Production
Technical requirements
Overview of model deployment
Serialization and persistence techniques
Scaling models for production
Monitoring and updating deployed models
Managing the model life cycle
Setting up deployment pipelines
Practical exercises in model deployment
Chapter 14: Unlock Your Exclusive Benefits Chevron down icon Chevron up icon
Chapter 14: Unlock Your Exclusive Benefits
Unlock this Book’s Free Benefits in 3 Easy Steps
Index Chevron down icon Chevron up icon
Index
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About the author

Profile icon John Sukup
John Sukup
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John Sukup is a seventeen-year data professional. His experience working with data spans from consumer market research to data science to ML and AI. He has over a decade of experience as an AI/ML cloud engineer and consultant at multiple international organizations including Levi Strauss, Cisco, Anaconda, and Ipsos. He has acted as the lead professional trainer for Fortune 100 organizations and has been featured in Forbes, Oracle, and Data Science Central. He currently acts as Managing Director and Founder at Expected X, an AI Solution Design and Consultancy as well as cohost of the Unriveted Podcast with his colleague Martin Miller.
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