| Description |
1 online resource |
| Summary |
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 deployment, discover tips from Kaggle masters, and much more! |
| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Subject |
Machine learning.
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Python (Computer program language)
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Apprentissage automatique. |
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Python (Langage de programmation) |
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Machine learning |
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Python (Computer program language) |
| Other Form: |
Print version : 9781839218354 |
| ISBN |
1839213809 |
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9781839213809 (electronic bk.) |
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(pbk.) |
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