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tidypredict: Run Predictions Inside the Database
It parses a fitted 'R' model object, and returns a formula in 'Tidy Eval' code that calculates the predictions. It works with several databases back-ends because it leverages 'dplyr' and 'dbplyr' for the final 'SQL' translation of the algorithm. It currently supports lm(), glm(), randomForest(), ranger(), earth(), xgb.Booster.complete(), cubist(), and ctree() models.
Getting started
Package overview
README.md
Create a regression spec - version 2
Create a tree spec - version 2
Cubist models
Database write-back
Generalized Linear Regression
Linear Regression
MARS models via the `earth` package
Non-R Models
Random Forest
Random Forest, using Ranger
Save and re-load models
XGBoost models
Browse package contents
Package details |
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| Author | Emil Hvitfeldt [aut, cre], Edgar Ruiz [aut], Max Kuhn [aut] |
| Maintainer | Emil Hvitfeldt <emil.hvitfeldt@posit.co> |
| License | MIT + file LICENSE |
| Version | 0.5.1 |
| URL | https://tidypredict.tidymodels.org https://github.com/tidymodels/tidypredict |
| Package repository | View on CRAN |
| Installation |
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