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Data Science Projects with Python - Second Edition

You're reading from  Data Science Projects with Python - Second Edition

Product type Book
Published in Jul 2021
Publisher Packt
ISBN-13 9781800564480
Pages 432 pages
Edition 2nd Edition
Languages
Author (1):
Stephen Klosterman Stephen Klosterman
Profile icon Stephen Klosterman

Table of Contents (9) Chapters

Preface
1. Data Exploration and Cleaning 2. Introduction to Scikit-Learn and Model Evaluation 3. Details of Logistic Regression and Feature Exploration 4. The Bias-Variance Trade-Off 5. Decision Trees and Random Forests 6. Gradient Boosting, XGBoost, and SHAP Values 7. Test Set Analysis, Financial Insights, and Delivery to the Client Appendix

Another Way of Growing Trees: XGBoost's grow_policy

In addition to limiting the maximum depth of trees using a max_depth hyperparameter, there is another paradigm for controlling tree growth: finding the node where a split would result in the greatest reduction in the loss function, and splitting this node, regardless of how deep it will make the tree. This may result in a tree with one or two very deep branches, while the other branches may not have grown very far. XGBoost offers a hyperparameter called grow_policy, and setting this to lossguide results in this kind of tree growth, while the depthwise option is the default and grows trees to an indicated max_depth, as we've done in Chapter 5, Decision Trees and Random Forests, and so far in this chapter. The lossguide grow policy is a newer option in XGBoost and mimics the behavior of LightGBM, another popular gradient boosting package.

To use the lossguide policy, it is necessary to set another hyperparameter we haven...

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