Hands-On Gradient Boosting with XGBoost and scikit-learn : Perform accessible machine learning and extreme gradient boosting with Python. /

This practical XGBoost guide will put your Python and scikit-learn knowledge to work by showing you how to build powerful, fine-tuned XGBoost models with impressive speed and accuracy. This book will help you to apply XGBoost's alternative base learners, use unique transformers for model deploy...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Wade, Corey
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Packt Publishing, 2020.
Temas:
Machine learning.
Python (Computer program language)
Apprentissage automatique.
Python (Langage de programmation)
Machine learning
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Cover
  • Copyright
  • About PACKT
  • Contributors
  • Table of Contents
  • Preface
  • Section 1: Bagging and Boosting
  • Chapter 1: Machine Learning Landscape
  • Previewing XGBoost
  • What is machine learning?
  • Data wrangling
  • Dataset 1
  • Bike rentals
  • Understanding the data
  • Correcting null values
  • Predicting regression
  • Predicting bike rentals
  • Saving data for future use
  • Declaring predictor and target columns
  • Understanding regression
  • Accessing scikit-learn
  • Silencing warnings
  • Modeling linear regression
  • XGBoost
  • XGBRegressor
  • Cross-validation
  • Predicting classification
  • What is classification?
  • Dataset 2
  • The census
  • Data wrangling
  • Logistic regression
  • The XGBoost classifier
  • Summary
  • Chapter 2: Decision Trees in Depth
  • Introducing decision trees with XGBoost
  • Exploring decision trees
  • First decision tree model
  • Inside a decision tree
  • Contrasting variance and bias
  • Tuning decision tree hyperparameters
  • Decision Tree regressor
  • Hyperparameters in general
  • Putting it all together
  • Predicting heart disease
  • a case study
  • Heart Disease dataset
  • Decision Tree classifier
  • Choosing hyperparameters
  • Narrowing the range
  • feature_importances_
  • Summary
  • Chapter 3: Bagging with Random Forests
  • Technical requirements
  • Bagging ensembles
  • Ensemble methods
  • Bootstrap aggregation
  • Exploring random forests
  • Random forest classifiers
  • Random forest regressors
  • Random forest hyperparameters
  • oob_score
  • n_estimators
  • warm_start
  • bootstrap
  • Verbose
  • Decision Tree hyperparameters
  • Pushing random forest boundaries
  • case study
  • Preparing the dataset
  • n_estimators
  • cross_val_score
  • Fine-tuning hyperparameters
  • Random forest drawbacks
  • Summary
  • Chapter 4: From Gradient Boosting to XGBoost
  • Technical requirements
  • From bagging to boosting
  • Introducing AdaBoost
  • Distinguishing gradient boosting
  • How gradient boosting works
  • Residuals
  • Learning how to build gradient boosting models from scratch
  • Building a gradient boosting model in scikit-learn
  • Modifying gradient boosting hyperparameters
  • learning_rate
  • Base learner
  • subsample
  • RandomizedSearchCV
  • XGBoost
  • Approaching big data
  • gradient boosting versus XGBoost
  • Introducing the exoplanet dataset
  • Preprocessing the exoplanet dataset
  • Building gradient boosting classifiers
  • Timing models
  • Comparing speed
  • Summary
  • Section 2: XGBoost
  • Chapter 5: XGBoost Unveiled
  • Designing XGBoost
  • Historical narrative
  • Design features
  • Analyzing XGBoost parameters
  • Learning objective
  • Building XGBoost models
  • The Iris dataset
  • The Diabetes dataset
  • Finding the Higgs boson
  • case study
  • Physics background
  • Kaggle competitions
  • XGBoost and the Higgs challenge
  • Data
  • Scoring
  • Weights
  • The model
  • Summary
  • Chapter 6: XGBoost Hyperparameters
  • Technical requirements
  • Preparing data and base models

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