nannyml.performance_calculation.metrics.regression module¶

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

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: Optional[str] = None)[source]¶

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: Optional[str] = None)[source]¶

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: Optional[str] = None)[source]¶

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: Optional[str] = None)[source]¶

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: Optional[str] = None)[source]¶

Root Mean Squared Logarithmic Error metric.

Creates a new RMSLE instance.

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

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.