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scikit-learn Cookbook - Third Edition

Name: scikit-learn Cookbook - Third Edition
Author: John Sukup
ISBN: 9781836644453

by John Sukup

December 2025

Intermediate to advanced

388 pages

8h 9m

English

Packt Publishing

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scikit-learn Cookbook
Contributors
About the author
About the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookInstalling Python libraries in virtual environments with requirements.txtInstalling the packagesVerifying installation (optional)Download the example code filesConventions usedGet in touchShare Your ThoughtsFree Benefits with Your Book
Chapter 1: Common Conventions and API Elements of scikit-learn
Technical requirementsIntroduction to scikit-learn’s design philosophyUnderstanding estimatorsTransformers and the transform() methodHandling custom estimators and transformersPipelines and workflow automationCommon attributes and methodsHyperparameter tuning with search methodsWorking with metadata: Tags and moreBest practices for API usage
Chapter 2: Pre-Model Workflow and Data Preprocessing
Technical requirementsThe impact of raw data on model performanceCommon data issuesCleaning and preparing dataHandling missing dataGetting readyHow to do it…There’s more…Scaling techniquesGetting readyHow to do it…There’s more…Encoding categorical variablesGetting readyHow to do it…Introduction to pipelines in scikit-learnWhat is a pipeline?Getting readyHow to do it…Visualizing pipelinesFeature engineeringUnderstanding feature engineeringGetting readyHow to do it…Practical exercises on data preprocessingHow to do it…
Chapter 3: Dimensionality Reduction Techniques
Technical requirementsIntroduction to dimensionality reductionWhy dimensionality reduction is essentialTheoretical foundations of dimensionality reductionTransforming datasets with PCAGetting readyHow to do it…How it works…Hands-on exercisesMaximizing class separability with LDAGetting readyHow to do it…How it works…Differences between PCA and LDAHands-on exercisest-SNE and data visualizationGetting readyHow to do it…How it works…Hands-on exercisesThere’s more…Guidelines for selecting dimensionality reduction techniquesPractical steps for selectionImpact on model performanceUnderstanding the trade-offsPractical exercises in dimensionality reductionExample 1: PCA with logistic regressionExample 2: t-SNE for visualization
Chapter 4: Building Models with Distance Metrics and Nearest Neighbors
Technical requirementsIntroduction to distance metricsUnderstanding KNNsGetting readyHow to do it…How it works…There’s more…Hands-on exercisesDistance metrics overviewGetting readyHow to do it…How it works…There’s more…Hands-on exercisesHyperparameter tuning in KNNGetting readyHow to do it…How it works…There’s more…Hands-on exercisesEvaluating KNN performanceGetting readyHow to do it…Understanding evaluation metricsThere’s more…Hands-on exercisesPractical exercises with KNN modelsExercise 1: Building a KNN classifierExercise 2: Tuning hyperparameters with grid searchExercise 3: Evaluating a KNN classifierThere’s more…
Chapter 5: Linear Models and Regularization
Technical requirementsIntroduction to linear modelsGetting readyHow to do it…How it works…There’s more…Hands-on exercisesRidge and Lasso regressionGetting readyHow to do it…How it works…Hands-on exercisesElasticNet and regularizationGetting readyHow to do it…How it works…There’s more…Hands-on exercisesRegularization theory and practiceHow to do it…There’s more…Regression and regularizationGetting readyHow to do it…How it works…There’s more…Hands-on exercisesPractical exercises with regularization techniquesExercise 1: Implementing ridge regressionExercise 2: Implementing Lasso regressionExercise 3: Implementing ElasticNet regression
Chapter 6: Advanced Logistic Regression and Extensions
Technical requirementsOverview of logistic regressionGetting readyHow to do it…How it works…Hands-on exercisesMulticlass classification techniquesGetting readyHow to do it…How it works…There’s more…Hands-on exercisesRegularization in logistic regressionGetting readyHow to do it…How it works…There’s more…Hands-on exercisesMultilabel classification conceptsGetting readyHow to do it…How it works…There’s more…Hands-on exercisesModel evaluation metricsGetting readyHow to do it…How it works…There’s more…Hands-on exercisesPractical exercises with advanced logistic regressionExercise 1: Building a regularized logistic regression modelExercise 2: Evaluating multiclass logistic regressionExercise 3: Visualizing logistic regression results

Chapter 7: Support Vector Machines and Kernel Methods
Technical requirementsIntroduction to SVMsGetting readyHow to do it…How it works…Hands-on exercisesKernel functions and their applicationsGetting readyHow to do it…How it works...There’s more...Hands-on exercisesTuning SVM parametersGetting readyHow to do It...How it works...There’s more...Hands-on exercisesSVMs in high-dimensional spacesGetting readyHow to do it...How it works...There’s more...Hands-on exercisesEvaluating SVM modelsGetting readyHow to do it...How it works...There’s more...Hands-on exercisesPractical exercises with SVMsExercise 1: Building a simple SVM classifierExercise 2: Tuning SVM parameters with grid searchExercise 3: Visualizing SVM decision boundaries
Chapter 8: Tree-Based Algorithms and Ensemble Methods
Technical requirementsIntroduction to decision treesGetting readyHow to do it…How it works…There’s more...Hands-on exercisesRandom forests and baggingGetting readyHow to do it...How it works...There’s more...Hands-on exercisesGradient boosting machinesGetting readyHow to do it...How it works...There’s more...Hands-on exercisesHyperparameter tuning for trees and ensemblesGetting readyHow to do it...How it works...Hands-on exercisesComparing ensemble methodsGetting readyHow to do it...How it works...There’s more...Hands-on exercisesPractical exercises with tree-based modelsExercise 1: Building and evaluating a decision tree classifierExercise 2: Hyperparameter tuning with random forestsExercise 3: Comparing gradient boosting and random forest
Chapter 9: Text Processing and Multiclass Classification
Technical requirementsIntroduction to text processingGetting readyHow to do it...How it works...There’s more...Hands-on exercisesText vectorization techniquesGetting readyHow to do it...How it works...There’s more...Hands-on exercisesFeature extraction from textGetting readyHow to do it...How it works...There’s more...Hands-on exercisesImplementing text classification modelsGetting readyHow to do it...How it works...There’s more...Hands-on exercisesMulticlass classification strategiesGetting readyHow to do it...How it works...There’s more...Hands-on exercisesEvaluating text modelsGetting readyHow to do it...How it works...There’s more...Hands-on exercisesPractical exercises in text processingExercise 1: Preprocessing and vectorizing textExercise 2: Feature extraction with n-gramsExercise 3: Building and evaluating a multiclass classifier
Chapter 10: Clustering Techniques
Technical requirementsIntroduction to clusteringGetting readyHow to do it...How it works...Hands-on exercisesK-means clusteringGetting readyHow to do it...How it works...There’s more...Hands-on exercisesHierarchical clusteringGetting readyHow to do it...How it works...There’s more...Hands-on exercisesDensity-based clustering with DBSCANGetting readyHow to do it...How it works...There’s more...Hands-on exercisesCluster evaluation metricsGetting readyHow to do it...How it works...There’s more...Hands-on exercisesChoosing the right clustering algorithmGetting readyHow to do it...How it works...There’s more...Hands-on exercisesAdvanced clustering techniquesGetting readyHow to do it...How it works...There’s more...Hands-on exercisesPractical exercises with clustering modelsExercise 1: Clustering with K-means on the Iris datasetExercise 2: Comparing DBSCAN and K-means on moon dataExercise 3: Clustering high-dimensional data with PCA and GMMs
Chapter 11: Novelty and Outlier Detection
Technical requirementsIntroduction to outlier and novelty detectionGetting readyHow to do it...How it works...There’s more...Hands-on exercisesUnderstanding Isolation ForestGetting readyHow to do it...How it works...There’s more...Hands-on exercisesOne-Class SVM for novelty detectionGetting readyHow to do it...How it works...There’s more...Hands-on exercisesDetecting outliers with LOFGetting readyHow to do it...How it works...There’s more...Hands-on exercisesEvaluating outlier detection modelsGetting readyHow to do it...How it works...There’s more...Hands-on exercisesHandling detected outliersGetting readyHow to do it...How it works...There’s more...Hands-on exercisesChoosing the right detection techniqueGetting readyHow to do it...How it works...There’s more...Hands-on exercisesPractical exercises in novelty and outlier detectionExercise 1: Applying Isolation Forest to a real-world datasetExercise 2: Using LOF on a network intrusion datasetExercise 3: One-Class SVM for manufacturing sensor data
Chapter 12: Cross-Validation and Model Evaluation Techniques
Technical requirementsIntroduction to cross-validationGetting readyHow to do it...How it works...Hands-on exercisesAdvanced cross-validation methodsGetting readyHow to do it...How it works...Hands-on exercisesImplementing cross-validation in scikit-learnGetting readyHow to do it...How it works...Hands-on exercisesModel selection techniquesGetting readyHow to do it...How it works...Hands-on exercisesEvaluating model generalizabilityGetting readyHow to do it...How it works...Hands-on exercisesPractical exercises in cross-validation and evaluationExercise 1: Cross-validating a logistic regression modelExercise 2: Tuning hyperparameters with grid searchExercise 3: Assessing model generalization with learning and validation curves
Chapter 13: Deploying scikit-learn Models in Production
Technical requirementsOverview of model deploymentGetting readyHow to do it...How it works...There’s more...Hands-on exercisesSerialization and persistence techniquesGetting readyHow to do it...How it works...There’s more...Hands-on exercisesScaling models for productionGetting readyHow to do it...How it works...There’s more...Hands-on exercisesMonitoring and updating deployed modelsGetting readyHow to do it...How it works...There’s more...Hands-on exercisesManaging the model life cycleGetting readyHow to do it...How it works...There’s more...Hands-on exercisesSetting up deployment pipelinesGetting readyHow to do it...How it works...There’s more...Hands-on exercisesPractical exercises in model deploymentExercise 1: Saving and reloading a model pipeline for deploymentExercise 2: Monitoring model accuracy over timeExercise 3: Automating deployment checks using validation thresholds
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Content preview from scikit-learn Cookbook - Third Edition

11 Novelty and Outlier Detection

The models we build with scikit-learn can be quite impressive with their predictive capabilities, but they are not infallible. A machine learning (ML) model is only as good as the data it is trained on, and if that data is messy or not representative of the phenomenon we are trying to model, our predictions will be less than optimal. Today, this has even become a new tactic among the hacker community: data poisoning, or maliciously tampering with data used for training to break a model’s capabilities. This means that having a robust set of tools for identifying data anomalies is even more important in a world that has become highly reliant on data-based solutions and products.

This chapter will cover techniques ...

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scikit-learn Cookbook - Third Edition

by John Sukup

11

Novelty and Outlier Detection

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.