AutoScore_rank {AutoScore}R Documentation

AutoScore STEP(i): Rank variables with machine learning (AutoScore Module 1)

Description

AutoScore STEP(i): Rank variables with machine learning (AutoScore Module 1)

Usage

AutoScore_rank(train_set, ntree = 100)

Arguments

train_set

A processed data.frame that contains data to be analyzed, for training.

ntree

Number of trees in the random forest (Default: 100).

Details

The first step in the AutoScore framework is variable ranking. We use random forest (RF), an ensemble machine learning algorithm, to identify the top-ranking predictors for subsequent score generation. This step correspond to Module 1 in the AutoScore paper.

Value

Returns a vector containing the list of variables and its ranking generated by machine learning (random forest)

References

See Also

AutoScore_parsimony, AutoScore_weighting, AutoScore_fine_tuning, AutoScore_testing, Run vignette("Guide_book", package = "AutoScore") to see the guidebook or vignette.

Examples

# see AutoScore Guidebook for the whole 5-step workflow
data("sample_data")
names(sample_data)[names(sample_data) == "Mortality_inpatient"] <- "label"
ranking <- AutoScore_rank(sample_data, ntree = 50)

[Package AutoScore version 0.2.0 Index]