predict_model

vector of uncalibrated predictions

calibration model to be used for the calibration. Can be the output of <code><a rd-options="" href="/link/build_BBQ?package=CalibratR&version=0.1.2" data-mini-rdoc="CalibratR::build_BBQ">build_BBQ</a></code>,<code><a rd-options="" href="/link/build_hist_binning?package=CalibratR&version=0.1.2" data-mini-rdoc="CalibratR::build_hist_binning">build_hist_binning</a></code> or <code><a rd-options="" href="/link/build_GUESS?package=CalibratR&version=0.1.2" data-mini-rdoc="CalibratR::build_GUESS">build_GUESS</a></code>.

calibration_model

minimum value of the original data set

maximum value of the original data set

mean value of the original data set

mean

specify if the model was build on original (=0), scaled(=1) or transformed (=2) data

inputtype

calibrates the uncalibrated predictions <code>new</code> using <code>calibration_model</code>.

Transforms your uncalibrated Machine Learning scores to well-calibrated prediction estimates that can be interpreted as probability estimates. The implemented BBQ (Bayes Binning in Quantiles) model is taken from Naeini (2015, ISBN:0-262-51129-0). Please cite this paper: Schwarz J and Heider D, Bioinformatics 2019, 35(14):2458-2465.

Dominik Heider

CalibratR

Mapping ML Scores to Calibrated Predictions

predict_model function

calibration model to be used for the calibration. Can be the output of <code><a rd-options='' href='build_BBQ'>build_BBQ</a></code>,<code><a rd-options='' href='build_hist_binning'>build_hist_binning</a></code> or <code><a rd-options='' href='build_GUESS'>build_GUESS</a></code>.

predict_model: predict_model

Description

Usage

Arguments

Value