nannyml.performance_calculation.metrics.regression module

class nannyml.performance_calculation.metrics.regression.MAE(y_true: str, y_pred: str, y_pred_proba: str | None = None)[source]

Bases: RegressionMetric

Mean Absolute Error metric.

Creates a new MAE instance.

class nannyml.performance_calculation.metrics.regression.MAPE(y_true: str, y_pred: str, y_pred_proba: str | None = None)[source]

Bases: RegressionMetric

Mean Absolute Percentage Error metric.

Creates a new MAPE instance.

class nannyml.performance_calculation.metrics.regression.MSE(y_true: str, y_pred: str, y_pred_proba: str | None = None)[source]

Bases: RegressionMetric

Mean Squared Error metric.

Creates a new MSE instance.

class nannyml.performance_calculation.metrics.regression.MSLE(y_true: str, y_pred: str, y_pred_proba: str | None = None)[source]

Bases: RegressionMetric

Mean Squared Logarithmic Error metric.

Creates a new MSLE instance.

class nannyml.performance_calculation.metrics.regression.RMSE(y_true: str, y_pred: str, y_pred_proba: str | None = None)[source]

Bases: RegressionMetric

Root Mean Squared Error metric.

Creates a new RMSE instance.

class nannyml.performance_calculation.metrics.regression.RMSLE(y_true: str, y_pred: str, y_pred_proba: str | None = None)[source]

Bases: RegressionMetric

Root Mean Squared Logarithmic Error metric.

Creates a new RMSLE instance.

class nannyml.performance_calculation.metrics.regression.RegressionMetric(*args, **kwargs)[source]

Bases: Metric, ABC

Creates a new Metric instance.

Parameters:
  • display_name (str) – The name of the metric. Used to display in plots. If not given this name will be derived from the calculation_function.

  • column_name (str) – The name used to indicate the metric in columns of a DataFrame.

  • upper_threshold_limit (float, default=None) – An optional upper threshold for the performance metric.

  • lower_threshold_limit (float, default=None) – An optional lower threshold for the performance metric.