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.