{"id":252177,"date":"2026-07-13T01:49:29","date_gmt":"2026-07-12T22:49:29","guid":{"rendered":"https:\/\/1kitap1.com\/en\/applied-machine-learning-jason-hodson-1\/"},"modified":"2026-07-13T01:49:29","modified_gmt":"2026-07-12T22:49:29","slug":"applied-machine-learning-jason-hodson-1","status":"publish","type":"post","link":"https:\/\/1kitap1.com\/en\/applied-machine-learning-jason-hodson-1\/","title":{"rendered":"Applied Machine Learning – Jason Hodson (1)"},"content":{"rendered":"
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When tuning hyperparameters to balance performance and overfitting, we\u2019ll still want to consider n_estimators, max_depth, and min_samples_split. However, now we\u2019ll also introduce two new hyperparameters: learning_rate and subsample. The Problem of Predictive Power As mentioned earlier, more predictive power comes with more problems. In this specific dataset example, you likely wouldn\u2019t use a GBM, as it\u2019s too powerful a model. For the sake of the example and continuity with the decision tree and random forest sections, we\u2019ll continue to use the same data.<\/p>\n

You\u2019ll see the performance impacts of the GBM later on in our use cases, so focus on learning the hyperparameters and understanding their impact as you go through this simpler example. The learning_rate hyperparameter adjusts how much each tree contributes to the final model. The higher the number, the faster the model runs because learning is sped up\u2014but then you risk overfitting. The risk of overfitting is reduced with a lower value. The lowest value is technically 0, and there is no upper limit on what you can input.<\/p>\n

Think about raising or lowering the learning rate as a trade-off in how quickly the model makes an assumption based on the data it sees. A high learning rate means the model is more willing to trust the data it sees right away. A lower learning rate makes the model more skeptical of the data you give it. This runs the risk that your model never learns new information that may be valuable. The subsample hyperparameter introduces randomness into each iteration of your model, which is another way to reduce overfitting.<\/p>\n

This number represents a percentage of the rows that each iteration of the model can use, so it\u2019s less abstract than the learning_rate hyperparameter. Let\u2019s start with some simple adjustments to these new hyperparameters to see how the results are impacted, as shown in Listing 5.86.<\/p>\n

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Production E-Book Kelly O\u2019Callaghan Typesetting E-Book III-satz, Germany We hope that you liked this e-book. Please share your feedback with us and read the Service Pages to find out how to contact us. The Library of Congress Cataloging-in-Publication Control Number for the printed edition is as follows: 2025053188 ISBN 978-1-4932-2758-7 (print) ISBN 978-1-4932-2759-4 (e-book) ISBN 978-1-4932-2760-0 (print and e-book) \u00a9 2026 by Rheinwerk Publishing Inc., Boston (MA) 1st edition 2026 1kitap1.com\/en Notes on Usage This e-book is protected by copyright.<\/p>\n

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You can find more recommendations on the customization of the screen layout on the Service Pages. 1kitap1.com\/en Table of Contents Notes on Usage Table of Contents Preface 1 Introduction 1.1 Aligning on Nomenclature 1.2 Learning to Google (or Prompt) 1.2.1 What Can You Find with Google?<\/p>\n<\/blockquote>\n

This is a short excerpt from the opening of “” by Unknown, quoted for review and introduction purposes. All rights belong to the copyright holders.<\/em><\/p>\n

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