ISBN: 9798192512265 - the predictive modeling workflow with python and scikit-learn: apply feature engineering, model selection, parameter tuning, and performance improvement techniques (6 Ergebnisse)

Taschenbuch. Zustand: Neu. Neuware.

Zustand: New. Print on Demand.

PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Zustand: new. Paperback. Training a predictive model can take only a few lines of code. Building one you can actually trust requires much more.The Predictive Modeling Workflow with Python and Scikit-Learn teaches you how to approach structured-data prediction as a complete engineering process, from defining the problem and auditing raw information to comparing algorithms, tuning carefully, evaluating honestly, and preparing the final solution for real use.Instead of focusing on isolated algorithms, this book shows how all the important pieces fit together. You will learn why validation strategy matters, how leakage creates misleading results, when simpler approaches outperform unnecessary complexity, and how reproducible pipelines make experimentation safer and easier to maintain.Inside, you will learn how to: Translate real business questions into clearly defined prediction tasksDefine targets, observation units, prediction timing, and success criteriaAudit structured datasets before training beginsHandle missing values, duplicates, extreme values, groups, and time-dependent observationsDesign training, validation, and test splits correctlyPrevent subtle forms of information leakageEstablish meaningful baselines before increasing complexityPreprocess numerical and categorical variables safelyBuild reusable pipelines and column-specific transformationsCreate useful ratios, interactions, temporal variables, and custom transformationsReduce unnecessary dimensions with PCA and other selection methodsBuild linear, ridge, lasso, and elastic-net regression solutionsCompare logistic regression, nearest-neighbor methods, and support vector machinesBuild decision trees, random forests, extremely randomized trees, and gradient-boosting predictorsCompare competing approaches using consistent cross-validationUse learning curves and validation curves to diagnose weaknessesPerform grid search, randomized search, and successive-halving searches efficientlyAvoid overfitting during extensive experimentationEvaluate regression and classification results using appropriate metricsWork with confusion matrices, probability calibration, decision thresholds, and error analysisInspect variable importance and partial dependenceImprove training speed, prediction latency, throughput, and memory usagePreserve fitted pipelines for later inferenceDocument dependencies, assumptions, input requirements, and deployment constraintsA final end-to-end project brings the complete process together, showing how problem definition, dataset auditing, preprocessing, validation, algorithm comparison, search, final evaluation, reproducibility, persistence, and production handoff form one coherent system.This book is suitable for beginners, students, analysts, career switchers, software developers, and working professionals who want practical competence with structured-data prediction.Basic Python and familiarity with rows, columns, and DataFrames are helpful, but previous machine-learning experience and advanced mathematics are not required.If you already know how to call fit() and predict() but want to understand how reliable real-world predictive work is actually organized, this book provides the missing framework.Move beyond isolated experiments and learn to build prediction workflows that are reproducible, explainable, defensible, and ready for real decisions. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Zustand: new. Paperback. Training a predictive model can take only a few lines of code. Building one you can actually trust requires much more.The Predictive Modeling Workflow with Python and Scikit-Learn teaches you how to approach structured-data prediction as a complete engineering process, from defining the problem and auditing raw information to comparing algorithms, tuning carefully, evaluating honestly, and preparing the final solution for real use.Instead of focusing on isolated algorithms, this book shows how all the important pieces fit together. You will learn why validation strategy matters, how leakage creates misleading results, when simpler approaches outperform unnecessary complexity, and how reproducible pipelines make experimentation safer and easier to maintain.Inside, you will learn how to: Translate real business questions into clearly defined prediction tasksDefine targets, observation units, prediction timing, and success criteriaAudit structured datasets before training beginsHandle missing values, duplicates, extreme values, groups, and time-dependent observationsDesign training, validation, and test splits correctlyPrevent subtle forms of information leakageEstablish meaningful baselines before increasing complexityPreprocess numerical and categorical variables safelyBuild reusable pipelines and column-specific transformationsCreate useful ratios, interactions, temporal variables, and custom transformationsReduce unnecessary dimensions with PCA and other selection methodsBuild linear, ridge, lasso, and elastic-net regression solutionsCompare logistic regression, nearest-neighbor methods, and support vector machinesBuild decision trees, random forests, extremely randomized trees, and gradient-boosting predictorsCompare competing approaches using consistent cross-validationUse learning curves and validation curves to diagnose weaknessesPerform grid search, randomized search, and successive-halving searches efficientlyAvoid overfitting during extensive experimentationEvaluate regression and classification results using appropriate metricsWork with confusion matrices, probability calibration, decision thresholds, and error analysisInspect variable importance and partial dependenceImprove training speed, prediction latency, throughput, and memory usagePreserve fitted pipelines for later inferenceDocument dependencies, assumptions, input requirements, and deployment constraintsA final end-to-end project brings the complete process together, showing how problem definition, dataset auditing, preprocessing, validation, algorithm comparison, search, final evaluation, reproducibility, persistence, and production handoff form one coherent system.This book is suitable for beginners, students, analysts, career switchers, software developers, and working professionals who want practical competence with structured-data prediction.Basic Python and familiarity with rows, columns, and DataFrames are helpful, but previous machine-learning experience and advanced mathematics are not required.If you already know how to call fit() and predict() but want to understand how reliable real-world predictive work is actually organized, this book provides the missing framework.Move beyond isolated experiments and learn to build prediction workflows that are reproducible, explainable, defensible, and ready for real decisions. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…