AutoScore_rank_Ordinal: AutoScore STEP (i) for ordinal outcomes: Generate variable...

In AutoScore: An Interpretable Machine Learning-Based Automatic Clinical Score Generator

AutoScore STEP (i) for ordinal outcomes: Generate variable ranking list by machine learning (AutoScore Module 1)

Description

AutoScore STEP (i) for ordinal outcomes: Generate variable ranking list by machine learning (AutoScore Module 1)

Usage

AutoScore_rank_Ordinal(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) for multiclass classification to identify the top-ranking predictors for subsequent score generation. This step corresponds to Module 1 in the AutoScore-Ordinal paper.

Value

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

References

• Breiman, L. (2001), Random Forests, Machine Learning 45(1), 5-32

• Saffari SE, Ning Y, Feng X, Chakraborty B, Volovici V, Vaughan R, Ong ME, Liu N, AutoScore-Ordinal: An interpretable machine learning framework for generating scoring models for ordinal outcomes, arXiv:2202.08407

See Also

AutoScore_parsimony_Ordinal, AutoScore_weighting_Ordinal, AutoScore_fine_tuning_Ordinal, AutoScore_testing_Ordinal.

Examples

## Not run: 
# see AutoScore-Ordinal Guidebook for the whole 5-step workflow
data("sample_data_ordinal") # Output is named `label`
ranking <- AutoScore_rank_ordinal(sample_data_ordinal, ntree = 50)

## End(Not run)

AutoScore documentation built on Oct. 16, 2022, 1:06 a.m.