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Update index.md

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Alejandro Moreo Fernandez 2024-09-17 11:58:56 +02:00 committed by GitHub
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@ -24,19 +24,18 @@ The following script fetches a dataset of tweets, trains, applies, and evaluates
```python ```python
import quapy as qp import quapy as qp
from sklearn.linear_model import LogisticRegression
dataset = qp.datasets.fetch_twitter('semeval16') dataset = qp.datasets.fetch_UCIBinaryDataset("yeast")
training, test = dataset.train_test
# create an "Adjusted Classify & Count" quantifier # create an "Adjusted Classify & Count" quantifier
model = qp.method.aggregative.ACC(LogisticRegression()) model = qp.method.aggregative.ACC()
model.fit(dataset.training) model.fit(training)
estim_prevalence = model.quantify(dataset.test.instances) estim_prevalence = model.quantify(test.X)
true_prevalence = dataset.test.prevalence() true_prevalence = test.prevalence()
error = qp.error.mae(true_prevalence, estim_prevalence) error = qp.error.mae(true_prevalence, estim_prevalence)
print(f'Mean Absolute Error (MAE)={error:.3f}') print(f'Mean Absolute Error (MAE)={error:.3f}')
``` ```