book

Machine Learning with LightGBM and Python

Name: Machine Learning with LightGBM and Python
Author: Andrich van Wyk
ISBN: 9781800564749

by Andrich van Wyk

September 2023

Intermediate to advanced

252 pages

6h 26m

English

Packt Publishing

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Machine Learning with LightGBM and Python
ContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Part 1: Gradient Boosting and LightGBM Fundamentals
Chapter 1: Introducing Machine Learning
Technical requirementsWhat is machine learning?Machine learning paradigmsIntroducing models, datasets, and supervised learningModelsHyperparametersDatasetsOverfitting and generalizationSupervised learningModel performance metricsA modeling exampleDecision tree learningEntropy and information gainBuilding a decision tree using C4.5Overfitting in decision treesBuilding decision trees with scikit-learnDecision tree hyperparametersSummaryReferences
Chapter 2: Ensemble Learning – Bagging and Boosting
Technical requirementsEnsemble learningBagging and random forestsRandom forestGradient-boosted decision treesGradient descentGradient boostingGradient-boosted decision tree hyperparametersGradient boosting in scikit-learnAdvanced boosting algorithm – DARTSummaryReferences
Chapter 3: An Overview of LightGBM in Python
Technical requirementsIntroducing LightGBMLightGBM optimizationsHyperparametersLimitations of LightGBMGetting started with LightGBM in PythonLightGBM Python APILightGBM scikit-learn APIBuilding LightGBM modelsCross-validationParameter optimizationPredicting student academic successSummaryReferences
Chapter 4: Comparing LightGBM, XGBoost, and Deep Learning
Technical requirementsAn overview of XGBoostComparing XGBoost and LightGBMPython XGBoost exampleDeep learning and TabTransformersWhat is deep learning?Introducing TabTransformersComparing LightGBM, XGBoost, and TabTransformersPredicting census incomeDetecting credit card fraudSummaryReferences
Part 2: Practical Machine Learning with LightGBM
Chapter 5: LightGBM Parameter Optimization with Optuna
Technical requirementsOptuna and optimization algorithmsIntroducing OptunaOptimization algorithmsPruning strategiesOptimizing LightGBM with OptunaAdvanced Optuna featuresSummaryReferences
Chapter 6: Solving Real-World Data Science Problems with LightGBM
Technical requirementsThe data science life cycleDefining the data science life cyclePredicting wind turbine power generation with LightGBMProblem definitionData collectionData preparationEDAModelingModel deploymentCommunicating resultsClassifying individual credit scores with LightGBMProblem definitionData collectionData preparationEDAModelingModel deployment and resultsSummaryReferences

Chapter 7: AutoML with LightGBM and FLAML
Technical requirementsAutomated machine learningAutomating feature engineeringAutomating model selection and tuningRisks of using AutoML systemsIntroducing FLAMLCost Frugal OptimizationBlendSearchFLAML limitationsCase study – using FLAML with LightGBMFeature engineeringFLAML AutoMLZero-shot AutoMLSummaryReferences
Part 3: Production-ready Machine Learning with LightGBM
Chapter 8: Machine Learning Pipelines and MLOps with LightGBM
Technical requirementsIntroducing machine learning pipelinesScikit-learn pipelinesUnderstanding MLOpsDeploying an ML pipeline for customer churnBuilding an ML pipeline using scikit-learnBuilding an ML API using FastAPIContainerizing our APIDeploying LightGBM to Google CloudSummary
Chapter 9: LightGBM MLOps with AWS SageMaker
Technical requirementsAn introduction to AWS and SageMakerAWSSageMakerSageMaker ClarifyBuilding a LightGBM ML pipeline with Amazon SageMakerSetting up a SageMaker sessionPreprocessing stepModel training and tuningEvaluation, bias, and explainabilityDeploying and monitoring the LightGBM modelResultsSummaryReferences
Chapter 10: LightGBM Models with PostgresML
Technical requirementsIntroducing PostgresMLLatency and round tripsGetting started with PostgresMLTraining modelsDeploying and predictionPostgresML dashboardCase study – customer churn with PostgresMLData loading and preprocessingTraining and hyperparameter optimizationPredictionsSummaryReferences
Chapter 11: Distributed and GPU-Based Learning with LightGBM
Technical requirementsDistributed learning with LightGBM and DaskGPU training for LightGBMSetting up LightGBM for the GPURunning LightGBM on the GPUSummaryReferences
Index
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Content preview from Machine Learning with LightGBM and Python

Part 3: Production-ready Machine Learning with LightGBM

In Part 3, we will delve into the practical applications of ML solutions in production environments. We will uncover the intricacies of machine learning pipelines, ensuring systematic data processing and model building for consistent results. MLOps, a confluence of DevOps and ML, takes center stage, highlighting the importance of deploying and maintaining robust ML systems in real-world scenarios. Through hands-on examples, we will explore the deployment of ML pipelines on platforms (like Google Cloud, Amazon SageMaker, and the innovative PostgresML) emphasizing the unique advantages each offers. Lastly, distributed computing and GPU-based training will be explored, showcasing methods to ...

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Machine Learning with LightGBM and Python

by Andrich van Wyk

Part 3: Production-ready Machine Learning with LightGBM

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