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Description

Computes Conformal p-Values.

Computes marginal conformal p-values using conformal prediction in binary classification tasks. Conformal prediction is a framework that augments machine learning algorithms with a measure of uncertainty, in the form of prediction regions that attain a user-specified level of confidence. This package specifically focuses on providing conformal p-values that can be used to assess the confidence of the classification predictions. For more details, see Tyagi and Guo (2023) <https://proceedings.mlr.press/v204/tyagi23a.html>.

Installation

To install the package, run:

devtools::install_github("ct364/conformalpvalue")

To use the function "naive_bayes_with_conformal_pvalues", use

# Train data with target variable "y"
# Calibration data with target variable "y"
# Test data without target variable "y"

result <- conformal_pvalues(train_data, calib_data, test_data, "y",method="naiveBayes")

This returns a matrix of conformal p-values.

Metadata

Version

0.1.0

License

Unknown

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