[{"_1":2,"_46":-5,"_47":-5},"loaderData",{"_3":4,"_14":15,"_44":-5,"_45":-5},"root",{"_5":-5,"_6":-5,"_7":-5,"_8":9,"_10":11},"user","shoppingCartId","toast","locale","en","constants",{"_12":13},"paypalClientId","AenrHU49peF-ZUH-Yq7AXLLNZYGUGlml474U16NCjB4ZznmQCEjiliN3whyuVZmkb7VxEzmTALgtKEKE","routes/_store+/_readers+/email_author.$slug/route",{"_16":17,"_43":-5},"book",{"_18":19,"_20":21,"_22":23,"_24":25,"_26":27,"_28":29},"id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNjM0NjQ","title","The Orange Book of Machine Learning - Green edition","state","published","contactAuthorMethod","email","aboutText","

This 'Green' edition is actively maintained and will be subject to updates. Purchasers have permanent access to the very latest version and (should) be notified by email when an updated version is available for download (latest version: 2.2.0).

The book covers the following topics in the context of supervised learning for tabular data:

1. Introduction

2. Statistics

3. Exploratory data analysis (EDA)

4. Data cleaning

5. Cross-validation

6. Interpolation and smoothing

7. Regression

8. Classification

9. Ensemble estimators

10. Hyperparameter optimization (HPO)

11. Feature engineering and selection

12. Why no neural networks/deep learning?

Book details:

Tech stack: python, pandas, scikit-learn, CatBoost, LightGBM, XGBoost
186 pages, 12pt font size
A4 page size (210 x 297 mm / 8.3 x 11.7 in) suitable for printing. The pages are `long' (i.e. narrow top and bottom margins) so as to facilitate uninterrupted scrolling when read on a tablet
Over 130 original figures
No generative AI whatsoever has been used in the writing of this book

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