tidypredict
Run Predictions Inside the Database

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tidypredict-package: tidypredict: Run Predictions Inside the Database

tidypredict-package: tidypredict: Run Predictions Inside the Database
In tidypredict: Run Predictions Inside the Database

tidypredict-packageR Documentation

tidypredict: Run Predictions Inside the Database

Description

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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. Dozens of model classes are supported; see the "Supported models" article at https://tidypredict.tidymodels.org/articles/models.html for the current list.

Author(s)

Maintainer: Emil Hvitfeldt emil.hvitfeldt@posit.co (ORCID)

Authors:

Other contributors:

  • Posit Software, PBC (ROR) [copyright holder, funder]

See Also

Useful links:


tidypredict documentation built on Oct. 18, 2026, 5:06 p.m.

Related to tidypredict-package in tidypredict...

tidypredict index
Package overview README.md bagger models bart models Create a regression spec - version 2 Create a tree spec - version 2 Cubist models Database write-back Decision trees, using C5.0 Decision trees, using rpart fda models Float precision at split boundaries Generalized Linear Regression glmnet models Gradient boosting, using H2O How tidypredict generates tree formulas lda models LightGBM models Linear Regression MARS models via the `earth` package mixOmics PLS models Model-based boosting, using mboost multinom models NaiveBayes models nnet models Non-R Models nullmodel models Oblique Random Forest, using aorsf qda models Random Forest Random Forest - partykit Random Forest, using Ranger RuleFit, using xrf Save and re-load models sda models sparsediscrim models Supported models Support vector machines, using kernlab XGBoost models

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