multi_model_1 {manymodelr}

Simultaneously train and predict on new data.

Description

This function provides a convenient way to train several model types. It allows a user to predict on new data and depending on the metrics, the user is able to decide which model predictions to finally use. The models are built based on Max Kuhn's models in the caret package.

Usage

multi_model_1(
  old_data,
  yname,
  xname,
  method = NULL,
  metric = NULL,
  control = NULL,
  new_data = NULL,
  ...
)

Arguments

old_data

The data holding the training dataset

yname

The outcome variable

xname

The predictor variable(s)

method

A vector containing methods to be used as defined in the caret package

metric

One of several metrics. Accuracy,RMSE,MAE,etc

control

See caret ?trainControl for details.

new_data

A data set to validate the model or for which predictions are required

...

Other arguments to caret's train function

Details

Most of the details of the parameters can be found in the caret package documentation. This function is meant to help in exploratory analysis to make an informed choice of the best models

Value

A list containing two objects. A tibble containing a summary of the metrics per model, a tibble containing predicted values and information concerning the model

References

Kuhn (2014), "Futility Analysis in the Cross-Validation of Machine Learning Models" http://arxiv.org/abs/1405.6974,

Kuhn (2008), "Building Predictive Models in R Using the caret" (http://www.jstatsoft.org/article/view/v028i05/v28i05.pold_data)

Examples

data("yields", package="manymodelr")
train_set<-createDataPartition(yields$normal,p=0.8,list=FALSE)
valid_set<-yields[-train_set,]
train_set<-yields[train_set,]
ctrl<-trainControl(method="cv",number=5)
set.seed(233)
m<-multi_model_1(train_set,"normal",".",c("knn","rpart"),
"Accuracy",ctrl,new_data =valid_set)
m$Predictions
m$Metrics
m$modelInfo