f1_measure module¶

The F1 to measure the quality of a classification algorithm.

The F1 is defined by

\[\begin{split}F_1 = 2\\frac{\\mathit{precision}\\mathit{recall}} {\\mathit{precision}+\\mathit{recall}}\end{split}\]

where \(\\mathit{precision}\) is the number of correctly predicted positives divided by the total number of predicted positives and \(\\mathit{recall}\) is the number of correctly predicted positives divided by the total number of true positives.

class gemseo.mlearning.quality_measures.f1_measure.F1Measure(algo, fit_transformers=True)[source]

Bases: BaseMLErrorMeasure

The F1 measure for machine learning.

Parameters:

algo (BaseMLClassificationAlgo) – A machine learning algorithm for classification.
fit_transformers (bool) –
Whether to re-fit the transformers when using resampling techniques. If False, use the transformers of the algorithm fitted from the whole learning dataset.

By default it is set to True.

SMALLER_IS_BETTER: ClassVar[bool] = False: Whether to minimize or maximize the measure.

algo: BaseMLClassificationAlgo: The machine learning algorithm whose quality we want to measure.

Examples using F1Measure¶

Advanced mixture of experts