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QuaPy
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Update README.md

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Alejandro Moreo Fernandez 2021-08-10 11:44:44 +02:00 committed by GitHub
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@ -21,7 +21,7 @@ pip install quapy
The following script fetchs a Twitter dataset, trains and evaluates an
_Adjusted Classify & Count_ model in terms of the _Mean Absolute Error_ (MAE)
between the class prevalences estimated for the test set and the true prevalences
between the class prevalence values estimated for the test set and the true prevalence values
of the test set.
```python
@ -34,20 +34,20 @@ dataset = qp.datasets.fetch_twitter('semeval16')
model = qp.method.aggregative.ACC(LogisticRegression())
model.fit(dataset.training)
estim_prevalences = model.quantify(dataset.test.instances)
true_prevalences = dataset.test.prevalence()
estim_prevalence = model.quantify(dataset.test.instances)
true_prevalence = dataset.test.prevalence()
error = qp.error.mae(true_prevalences, estim_prevalences)
error = qp.error.mae(true_prevalence, estim_prevalence)
print(f'Mean Absolute Error (MAE)={error:.3f}')
```
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
words, we would not be interested in estimating the class prevalence values 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. 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.
should be tested across samples characterized by different class prevalence values.
QuaPy implements sampling procedures and evaluation protocols that automate this endeavour.
See the [Wiki](https://github.com/HLT-ISTI/QuaPy/wiki) for detailed examples.
## Features