lorenzo.volpi
/
QuaPy
forked from moreo/QuaPy
1
0
Fork 0
Code Pull Requests Activity

readme update

This commit is contained in:
Alejandro Moreo Fernandez 2021-02-24 16:47:12 +01:00
parent 775417c8eb
commit f76a507e14
1 changed files with 11 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

16
README.md
View File

@ -36,10 +36,10 @@ error = qp.error.mae(true_prevalences, estim_prevalences)
print(f'Mean Absolute Error (MAE)={error:.3f}') print(f'Mean Absolute Error (MAE)={error:.3f}')
``` ```
Quantification is useful in scenarios of distribution shift. In other Quantification is useful in scenarios of prior probability shift. In other
words, we would not need to estimate the class prevalences of the test set if words, we would not be interested in estimating the class prevalences of the test set if
we could assume the IID assumption to hold, as this prevalence would simply coincide with the we could assume the IID assumption to hold, as this prevalence would simply coincide with the
class prevalence of the training set. That is to say, a Quantification model class prevalence of the training set. For this reason, any Quantification model
should be tested across samples characterized by different class prevalences. should be tested across samples characterized by different class prevalences.
QuaPy implements sampling procedures and evaluation protocols that automates this endeavour. QuaPy implements sampling procedures and evaluation protocols that automates this endeavour.
See the [Wiki](https://github.com/HLT-ISTI/QuaPy/wiki) for detailed examples. See the [Wiki](https://github.com/HLT-ISTI/QuaPy/wiki) for detailed examples.
@ -56,7 +56,7 @@ SVM-based variants for quantification, HDy, QuaNet, and Ensembles).
* 3 Reviews Sentiment datasets. * 3 Reviews Sentiment datasets.
* Native supports for binary and single-label scenarios of quantification. * Native supports for binary and single-label scenarios of quantification.
* Model selection functionality targeting quantification-oriented losses. * Model selection functionality targeting quantification-oriented losses.
* Plotting routines ("error-by-drift", "diagonal", and "bias" plots). * Visualization tools for analysing results.
## Requirements ## Requirements
@ -96,4 +96,10 @@ _AE_ and _RAE_.
## Wiki ## Wiki
Check out our [Wiki](https://github.com/HLT-ISTI/QuaPy/wiki) in which many examples Check out our [Wiki](https://github.com/HLT-ISTI/QuaPy/wiki) in which many examples
are provided. are provided:
* [Datasets](https://github.com/HLT-ISTI/QuaPy/wiki/Datasets)
* [Evaluation](https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation)
* [Methods](https://github.com/HLT-ISTI/QuaPy/wiki/Methods)
* [Model Selection](https://github.com/HLT-ISTI/QuaPy/wiki/Model-Selection)
* [Plotting](https://github.com/HLT-ISTI/QuaPy/wiki/Plotting)