classification

Accuracy(features=None)

Bases: SelectMixin, LazyMixin, Metric

Accuracy classification score.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
features	list[str] | None	The features to calculate the metric for. If None, the metric uses all features in the data.	None

Source code in src/flowcean/sklearn/metrics/classification.py

def __init__(
    self,
    features: list[str] | None = None,
) -> None:
    """Initialize metric.

    Args:
        features: The features to calculate the metric for. If None, the
            metric uses all features in the data.
    """
    super().__init__(features=features)

ClassificationReport(features=None)

Bases: SelectMixin, LazyMixin, Metric

Build a text report showing the main classification metrics.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
features	list[str] | None	The features to calculate the metric for. If None, the metric uses all features in the data.	None

Source code in src/flowcean/sklearn/metrics/classification.py

def __init__(
    self,
    features: list[str] | None = None,
) -> None:
    """Initialize metric.

    Args:
        features: The features to calculate the metric for. If None, the
            metric uses all features in the data.
    """
    super().__init__(features=features)

FBetaScore(*, beta=1.0, features=None)

Bases: SelectMixin, LazyMixin, Metric

F-beta score.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
beta	float	The beta parameter.	1.0
features	list[str] | None	The features to calculate the metric for. If None, the metric uses all features in the data.	None

Source code in src/flowcean/sklearn/metrics/classification.py

def __init__(
    self,
    *,
    beta: float = 1.0,
    features: list[str] | None = None,
) -> None:
    """Initialize metric.

    Args:
        beta: The beta parameter.
        features: The features to calculate the metric for. If None, the
            metric uses all features in the data.
    """
    super().__init__(features=features)
    self.beta = beta

PrecisionScore(features=None)

Bases: SelectMixin, LazyMixin, Metric

Precision classification score.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
features	list[str] | None	The features to calculate the metric for. If None, the metric uses all features in the data.	None

Source code in src/flowcean/sklearn/metrics/classification.py

def __init__(
    self,
    features: list[str] | None = None,
) -> None:
    """Initialize metric.

    Args:
        features: The features to calculate the metric for. If None, the
            metric uses all features in the data.
    """
    super().__init__(features=features)

Recall(features=None)

Bases: SelectMixin, LazyMixin, Metric

Recall classification score.

As defined by scikit-learn.

Initialize metric.

Parameters:

Name	Type	Description	Default
features	list[str] | None	The features to calculate the metric for. If None, the metric uses all features in the data.	None

Source code in src/flowcean/sklearn/metrics/classification.py

def __init__(
    self,
    features: list[str] | None = None,
) -> None:
    """Initialize metric.

    Args:
        features: The features to calculate the metric for. If None, the
            metric uses all features in the data.
    """
    super().__init__(features=features)