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probably

Introduction

probably contains tools to facilitate activities such as:

  • Conversion of probabilities to discrete class predictions.

  • Investigating and estimating optimal probability thresholds.

  • Inclusion of equivocal zones where the probabilities are too uncertain to report a prediction.

Installation

You can install probably from CRAN with:

install.packages("probably")

You can install the development version of probably from GitHub with:

devtools::install_github("topepo/probably")

Examples

Good places to look for examples of using probably are the vignettes.

  • vignette("equivocal-zones", "probably") discusses the new class_pred class that probably provides for working with equivocal zones.

  • vignette("where-to-use", "probably") discusses how probably fits in with the rest of the tidymodels ecosystem, and provides an example of optimizing class probability thresholds.

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Version

Install

install.packages('probably')

Monthly Downloads

2,135

Version

0.0.3

License

GPL-2

Issues

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Stars

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Maintainer

Davis Vaughan

Last Published

July 7th, 2019

Functions in probably (0.0.3)

append_class_pred

Add a class_pred column
class_pred

Create a class prediction object
levels.class_pred

Extract class_pred levels
probably-package

probably: Tools for Post-Processing Class Probability Estimates
as_class_pred

Coerce to a class_pred object
is_class_pred

Test if an object inherits from class_pred
make_class_pred

Create a class_pred vector from class probabilities
reexports

Objects exported from other packages
threshold_perf

Generate performance metrics across probability thresholds
reportable_rate

Calculate the reportable rate
locate-equivocal

Locate equivocal values
vec_proxy_equal.class_pred

Equality for class_pred
vec_cast.class_pred

Cast a class_pred vector to a specified type
vec_ptype2.class_pred

Find the common type for a class_pred and another object
species_probs

Predictions on animal species
segment_naive_bayes

Image segmentation predictions