README.md

In dsnavega/icmr: Inductive Confidence Machine for Regression

icmr: Inductive Confidence Machine for Regression

This packages provides an implementation of an Inductive Confidence Machine for Regression based on Ridge Regression. The telos of this package is to create a regression model that provides point estimates and reliable predictive/confidence intervals with minimal assumptions, which is particularly useful in domains where the degree of uncertainty of the prediction is as important as the prediction itself (point estimate). Consult the list references for more details on conformal prediction.

Install:

# install.packages("devtools")
devtools::install_github(repo = "dsnavega/icmr")

Usage

# Build a 'icmr' object
icmr.object <- icmr(x = x, y = y, control = icmr.control())

# Obtaining a prediction
prediction <- predict(object = icmr.object, newdata = z, alpha = 0.05)

In the Usage example x is assumed to be a matrix of numeric values and y a vector of numeric data.

References

1. Balasubramanian VN, Ho S-S, Vovk V (2016) Conformal Prediction for Reliable Machine Learning
2. Papadopoulos H (2012) Inductive Conformal Prediction: Theory and Application to Neural Networks. Tools Artif Intell. https://doi.org/10.5772/6078
3. Papadopoulos H (2015) Cross-Conformal prediction with ridge regression. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, pp 260–270
4. Norinder U, Carlsson L, Boyer S, et al (2014) Introducing Conformal Prediction in Predictive Modeling. A Transparent and Flexible Alternative To Applicability Domain Determination. J Chem Inf Model 54:1596–1603. https://doi.org/10.1021/ci5001168
5. Papadopoulos H, Vovk V, Gammerman A (2007) Conformal prediction with neural networks. Proc - Int Conf Tools with Artif Intell ICTAI 2:388–395. https://doi.org/10.1109/ICTAI.2007.47
6. Wang D, Wang P, Shi J (2018) A fast and efficient conformal regressor with regularized extreme learning machine. Neurocomputing 304:1–11. https://doi.org/10.1016/j.neucom.2018.04.012
7. Norinder U, Carlsson L, Boyer S, Eklund M (2015) Introducing conformal prediction in predictive modeling for regulatory purposes. A transparent and flexible alternative to applicability domain determination. Regul Toxicol Pharmacol 71:279–284. https://doi.org/10.1016/j.yrtph.2014.12.021
8. Shafer G, Vovk V (2007) A tutorial on conformal prediction
9. Papadopoulos H, Haralambous H (2011) Reliable prediction intervals with regression neural networks. Neural Networks 24:842–851. https://doi.org/10.1016/j.neunet.2011.05.008
10. Papadopoulos H (2015) Cross-Conformal prediction with ridge regression. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, pp 260–270

dsnavega/icmr documentation built on Oct. 25, 2021, 4:14 p.m.