Nothing
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(), rpart(), earth(), xgb.Booster.complete(), lgb.Booster(), catboost.Model(), cubist(), and ctree() models.
Getting started
Package overview
README.md
catboost models
Create a regression spec - version 2
Create a tree spec - version 2
Cubist models
Database write-back
Decision trees, using rpart
Float precision at split boundaries
Generalized Linear Regression
glmnet models
How tidypredict generates tree formulas
LightGBM models
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 |
|
|---|---|
| Author | Emil Hvitfeldt [aut, cre], Edgar Ruiz [aut], Max Kuhn [aut] |
| Maintainer | Emil Hvitfeldt <emil.hvitfeldt@posit.co> |
| License | MIT + file LICENSE |
| Version | 1.1.1 |
| URL | https://tidypredict.tidymodels.org https://github.com/tidymodels/tidypredict |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
|
Try the tidypredict package in your browser
Any scripts or data that you put into this service are public.
