nannyml.sampling_error.regression module
- nannyml.sampling_error.regression.mae_sampling_error(sampling_error_components, data) float [source]
Calculate Mean Absolute Error (MAE) sampling error for a chunk of data.
- Parameters:
sampling_error_components (a set of parameters that were derived from reference data.) –
data (the (analysis) data you want to calculate or estimate a metric for.) –
- Returns:
sampling_error
- Return type:
float
- nannyml.sampling_error.regression.mae_sampling_error_components(y_true_reference: Series, y_pred_reference: Series) Tuple [source]
Calculate sampling error components for Mean Absolute Error (MAE) using reference data.
- Parameters:
y_true_reference (pd.Series) – Target values for the reference dataset.
y_pred_reference (pd.Series) – Predictions for the reference dataset.
- Returns:
(std,)
- Return type:
Tuple[np.ndarray]
- nannyml.sampling_error.regression.mape_sampling_error(sampling_error_components, data) float [source]
Calculate Mean Absolute Percentage Error (MAPE) sampling error for a chunk of data.
- Parameters:
sampling_error_components (a set of parameters that were derived from reference data.) –
data (the (analysis) data you want to calculate or estimate a metric for.) –
- Returns:
sampling_error
- Return type:
float
- nannyml.sampling_error.regression.mape_sampling_error_components(y_true_reference: Series, y_pred_reference: Series) Tuple [source]
Calculate sampling error components for Mean Absolute Percentage Error (MAPE) using reference data.
- Parameters:
y_true_reference (pd.Series) – Target values for the reference dataset.
y_pred_reference (pd.Series) – Predictions for the reference dataset.
- Returns:
(std,)
- Return type:
Tuple[np.ndarray]
- nannyml.sampling_error.regression.mse_sampling_error(sampling_error_components, data) float [source]
Calculate Mean Squared Error (MSE) sampling error for a chunk of data.
- Parameters:
sampling_error_components (a set of parameters that were derived from reference data.) –
data (the (analysis) data you want to calculate or estimate a metric for.) –
- Returns:
sampling_error
- Return type:
float
- nannyml.sampling_error.regression.mse_sampling_error_components(y_true_reference: Series, y_pred_reference: Series) Tuple [source]
Calculate sampling error components for Mean Squared Error (MSE) using reference data.
- Parameters:
y_true_reference (pd.Series) – Target values for the reference dataset.
y_pred_reference (pd.Series) – Predictions for the reference dataset.
- Returns:
(std,)
- Return type:
Tuple[np.ndarray]
- nannyml.sampling_error.regression.msle_sampling_error(sampling_error_components, data) float [source]
Calculate Mean Squared Logarithmic Error (MSLE) sampling error for a chunk of data.
- Parameters:
sampling_error_components (a set of parameters that were derived from reference data.) –
data (the (analysis) data you want to calculate or estimate a metric for.) –
- Returns:
sampling_error
- Return type:
float
- nannyml.sampling_error.regression.msle_sampling_error_components(y_true_reference: Series, y_pred_reference: Series) Tuple [source]
Calculate sampling error components for Mean Squared Logarithmic Error (MSLE) using reference data.
- Parameters:
y_true_reference (pd.Series) – Target values for the reference dataset.
y_pred_reference (pd.Series) – Predictions for the reference dataset.
- Returns:
(std,)
- Return type:
Tuple[np.ndarray]
- nannyml.sampling_error.regression.rmse_sampling_error(sampling_error_components, data) float [source]
Calculate Root Mean Squared Error (RMSE) sampling error for a chunk of data.
- Parameters:
sampling_error_components (a set of parameters that were derived from reference data.) –
data (the (analysis) data you want to calculate or estimate a metric for.) –
- Returns:
sampling_error
- Return type:
float
- nannyml.sampling_error.regression.rmse_sampling_error_components(y_true_reference: Series, y_pred_reference: Series) Tuple [source]
Calculate sampling error components for Root Mean Squared Error (RMSE) using reference data.
- Parameters:
y_true_reference (pd.Series) – Target values for the reference dataset.
y_pred_reference (pd.Series) – Predictions for the reference dataset.
- Returns:
(std,)
- Return type:
Tuple[np.ndarray]
- nannyml.sampling_error.regression.rmsle_sampling_error(sampling_error_components, data) float [source]
Calculate Root Mean Squared Logarithmic Error (RMSLE) sampling error for a chunk of data.
- Parameters:
sampling_error_components (a set of parameters that were derived from reference data.) –
data (the (analysis) data you want to calculate or estimate a metric for.) –
- Returns:
sampling_error
- Return type:
float
- nannyml.sampling_error.regression.rmsle_sampling_error_components(y_true_reference: Series, y_pred_reference: Series) Tuple [source]
Calculate sampling error components for Root Mean Squared Logarithmic Error (RMSLE) using reference data.
- Parameters:
y_true_reference (pd.Series) – Target values for the reference dataset.
y_pred_reference (pd.Series) – Predictions for the reference dataset.
- Returns:
(std,)
- Return type:
Tuple[np.ndarray]