tidypredict: Run Predictions Inside the Database

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

Vignettes Man pages API and functions Files