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Ensemble Machine Learning Cookbook

You're reading from   Ensemble Machine Learning Cookbook Over 35 practical recipes to explore ensemble machine learning techniques using Python

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789136609
Length 336 pages
Edition 1st Edition
Languages
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Authors (2):
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Vijayalakshmi Natarajan Vijayalakshmi Natarajan
Author Profile Icon Vijayalakshmi Natarajan
Vijayalakshmi Natarajan
Dipayan Sarkar Dipayan Sarkar
Author Profile Icon Dipayan Sarkar
Dipayan Sarkar
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Toc

Table of Contents (14) Chapters Close

Preface 1. Get Closer to Your Data 2. Getting Started with Ensemble Machine Learning FREE CHAPTER 3. Resampling Methods 4. Statistical and Machine Learning Algorithms 5. Bag the Models with Bagging 6. When in Doubt, Use Random Forests 7. Boosting Model Performance with Boosting 8. Blend It with Stacking 9. Homogeneous Ensembles Using Keras 10. Heterogeneous Ensemble Classifiers Using H2O 11. Heterogeneous Ensemble for Text Classification Using NLP 12. Homogenous Ensemble for Multiclass Classification Using Keras 13. Other Books You May Enjoy

Implementing the extreme gradient boosting method for glass identification using XGBoost with scikit-learn

XGBoost stands for extreme gradient boosting. It is a variant of the gradient boosting machine that aims to improve performance and speed. The XGBoost library in Python implements the gradient boosting decision tree algorithm. The name gradient boosting comes from its us of the gradient descent algorithm to minimize loss when adding new models. XGBoost can handle both regression and classification tasks.

XGBoost is the algorithm of choice among those participating in Kaggle competitions because of its performance and speed of execution in difficult machine learning problems.

Some of the important parameters that are used in XGBoost are as follows:

  • n_estimators/ntrees: This specifies the number of trees to build. The default value is 50.
  • max_depth: This specifies the maximum...
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