An implementation of Precision@k compatible with Scikit-learn.

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An implementation of Precision@k compatible with Scikit-learn.

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Last edited Dec 12, 2021

Created on Dec 12, 2021

This example demonstrates a custom implementation of the Precision@k metric that integrates directly with Scikit-learn's array utilities. It shows how to compute precision for the top *k* predictions, assuming a binary classification task. The function uses `column_or_1d` to flatten input arrays and `type_of_target` to validate the binary format. It sorts the true labels by descending prediction scores using `np.argsort`, then calculates the proportion of true positives among the top *k* entries. The code relies on NumPy for sorting and summing, and it returns a scalar float representing the precision value.

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