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F1-score
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Description
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The F1-score is a combination of the precision and recall, which is defined by calculating the harmonic mean of the two metrics. Its mathematical equation is described as follows:

.. math::
   \text{F1-score} = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}}

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Tools and Libraries
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To illustrate the classification metrics, the **digits** dataset provided by the **scikit-learn** library will be used. It is a dataset of hand-written digits which contains 1797 samples and each sample is an 8x8-image.
The number of classes in this dataset is 10 (corresponding to the digits from 0 to 9). This dataset will be loaded and split in `train` and test `test` (20%) sets. As a classifier, a logistic regression model will be used and trained. After the training, the model's performance will be evaluated.

Python
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To compute the F1-score for a classification problem, the following example can be used.

Install **scikit-learn** using this command:
:: 
   pip install -U scikit-learn
   
.. literalinclude:: examples/CLASS_F1_SCORE/CLASS_F1_SCORE.py