IPMRF-package: Intervention in Prediction Measure (IPM) for Random Forests

In IPMRF: Intervention in Prediction Measure (IPM) for Random Forests

Description

It computes IPM for assessing variable importance for random forests. See I. Epifanio (2017). Intervention in prediction measure: a new approach to assessing variable importance for random forests. BMC Bioinformatics.

Details

Package:	IPMRF
Type:	Package
Version:	1.2
Date:	2017-08-09

Main Functions:

• ipmparty: IPM casewise with CIT-RF by party for OOB samples

• ipmpartynew: IPM casewise with CIT-RF by party for new samples

• ipmrf: IPM casewise with CART-RF by randomForest for OOB samples

• ipmrfnew: IPM casewise with CART-RF by randomForest for new samples

• ipmranger: IPM casewise with RF by ranger for OOB samples

• ipmrangernew: IPM casewise with RF by ranger for new samples

• ipmgbmnew: IPM casewise with GBM by gbm for new samples

Author(s)

Irene Epifanio, Stefano Nembrini

References

Pierola, A. and Epifanio, I. and Alemany, S. (2016) An ensemble of ordered logistic regression and random forest for child garment size matching. Computers & Industrial Engineering, 101, 455–465.

Epifanio, I. (2017) Intervention in prediction measure: a new approach to assessing variable importance for random forests. BMC Bioinformatics, 18, 230.

See Also

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1650-8

IPMRF documentation built on May 2, 2019, 6:42 a.m.