From a8ef7a6ed3b9616453a26ca19cbc398a85e495d7 Mon Sep 17 00:00:00 2001 From: Alejandro Moreo Date: Tue, 10 Aug 2021 11:44:44 +0200 Subject: [PATCH] Update README.md --- README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 9a64c4f..5f8c1f1 100644 --- a/README.md +++ b/README.md @@ -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 @@ -108,4 +108,4 @@ are provided: * [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) \ No newline at end of file +* [Plotting](https://github.com/HLT-ISTI/QuaPy/wiki/Plotting)