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The Kaggle Workbook

You're reading from   The Kaggle Workbook Self-learning exercises and valuable insights for Kaggle data science competitions

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Product type Paperback
Published in Feb 2023
Publisher Packt
ISBN-13 9781804611210
Length 172 pages
Edition 1st Edition
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Authors (2):
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Luca Massaron Luca Massaron
Author Profile Icon Luca Massaron
Luca Massaron
Konrad Banachewicz Konrad Banachewicz
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Konrad Banachewicz
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Toc

Examining the 4th place solution’s ideas from Monsaraida

There are many solutions available for the competition, mostly found on the competition Kaggle discussions pages. The top five methods of both challenges have also been gathered and published (except one because of proprietary rights) by the competition organizers themselves: https://github.com/Mcompetitions/M5-methods (by the way, reproducing the results of the winning submissions was a prerequisite for the collection of a competition prize).

Noticeably, all the Kagglers that placed in the higher ranks of the competitions have used, as their unique model type or in blended/stacked in ensembles, LightGBM because of its lesser memory usage and speed of computations, which gave it an advantage in the competition because of the large amount of times series to process and predict. But there are also other reasons for its success. Contrary to classical methods based on ARIMA, it doesn’t require relying on the analysis...

You have been reading a chapter from
The Kaggle Workbook
Published in: Feb 2023
Publisher: Packt
ISBN-13: 9781804611210
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