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Discussion on "Time Series Forecasting Lab (Part 4) - Hyperparameter Tuning" | Hashnode

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Time Series Forecasting Lab (Part 4) - Hyperparameter Tuning

Cover photo by christian buehner on Unsplash Go to R-bloggers for R news and tutorials contributed by hundreds of R bloggers. Introduction This is the fourth of a series of 6 articles about time series forecasting with panel data and ensemble stack...

Jan 15, 2022·20 min read·blog.bguarisma.comUpdated Jun 24, 2022Read the article

#machine-learning

Written by

Boris Guarisma

@bguarisma · Paris, France

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Data Science, AI & Quantum Computing

Avec curiosité et humilité, je partage ici mes explorations en IA, data science et au-delà. Un carnet de bord pour apprendre, douter, comprendre — loin du buzz, proche du vrai.

Responses4

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Oct 30, 2022

Hi Boris Guarisma, I believe you use update_role twice during the blog, the first is for row_id in part 2 and the second is for month in part 4

Do we need both variables defined as "ID" roles?

Oct 29, 2022

Hi Boris Guarisma, where do the _rds objects come from i.e. tuned_rf_rds, tuned_xgboost_rds, tuned_prophet_rds and tuned_prophet_fou_boost_rds? Thanks

Rizqi Ramadhan

Apr 13, 2022

Hi, do you know how to export the predicted results? thanks

Simona Jokubauskaite

Jan 25, 2022

Hi, model_spec_rf_tune, grid_phtxb_spec are not defined.

Boris Guarisma

Thank you Simona once again. The article has been corrected, "phtxb" should not be used and "model_spec_rf_tune" was for random forest, here we work with prophet boost.

Jan 25, 2022