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readme update

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Alejandro Moreo Fernandez 2021-02-24 16:47:12 +01:00
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@ -36,10 +36,10 @@ error = qp.error.mae(true_prevalences, estim_prevalences)
print(f'Mean Absolute Error (MAE)={error:.3f}')
```
Quantification is useful in scenarios of distribution shift. In other
words, we would not need to estimate the class prevalences of the test set if
Quantification is useful in scenarios of prior probability shift. In other
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
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.
QuaPy implements sampling procedures and evaluation protocols that automates this endeavour.
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.
* Native supports for binary and single-label scenarios of quantification.
* Model selection functionality targeting quantification-oriented losses.
* Plotting routines ("error-by-drift", "diagonal", and "bias" plots).
* Visualization tools for analysing results.
## Requirements
@ -96,4 +96,10 @@ _AE_ and _RAE_.
## Wiki
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)