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Java: Data Science Made Easy

You're reading from   Java: Data Science Made Easy Data collection, processing, analysis, and more

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Product type Course
Published in Jul 2017
Publisher Packt
ISBN-13 9781788475655
Length 734 pages
Edition 1st Edition
Languages
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Authors (3):
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Alexey Grigorev Alexey Grigorev
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Alexey Grigorev
Richard M. Reese Richard M. Reese
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Richard M. Reese
Jennifer L. Reese Jennifer L. Reese
Author Profile Icon Jennifer L. Reese
Jennifer L. Reese
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Toc

Table of Contents (29) Chapters Close

Title Page
Credits
Preface
1. Module 1 FREE CHAPTER
2. Getting Started with Data Science 3. Data Acquisition 4. Data Cleaning 5. Data Visualization 6. Statistical Data Analysis Techniques 7. Machine Learning 8. Neural Networks 9. Deep Learning 10. Text Analysis 11. Visual and Audio Analysis 12. Visual and Audio Analysis 13. Mathematical and Parallel Techniques for Data Analysis 14. Bringing It All Together 15. Module 2
16. Data Science Using Java 17. Data Processing Toolbox 18. Exploratory Data Analysis 19. Supervised Learning - Classification and Regression 20. Unsupervised Learning - Clustering and Dimensionality Reduction 21. Working with Text - Natural Language Processing and Information Retrieval 22. Extreme Gradient Boosting 23. Deep Learning with DeepLearning4J 24. Scaling Data Science 25. Deploying Data Science Models 26. Bibliography

Gradient Boosting Machines and XGBoost


Gradient Boosting Machines (GBM) is an ensembling algorithm. The main idea behind GBM is to take some base model and then  fit this model, over and over, to the data, gradually improving the performance. It is different from Random Forest models because GBM tries to improve the results at each step, while random forest builds multiple independent models and takes their average.

The main idea behind GBM can be best illustrated with a Linear Regression example. To fit several linear regressions to data, we can do the following:

  1. Fit the base model to the original data.
  2. Take the difference between the target value and the prediction of the first model (we call it the residuals of Step 1) and use this for training the second model.
  3. Take the difference between the residuals of step 1 and predictions of step 2 (this is the residuals of Step 2) and fit the 3rd model.
  4. Continue until you train N models.
  5. For predicting, sum up the predictions of all individual models...
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