mlens.metrics.metrics module¶
ML-ENSEMBLE
| author: | Sebastian Flennerhag |
|---|---|
| copyright: | 2017 |
| license: | MIT |
Scoring functions.
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mlens.metrics.metrics.mape(y, p)[source]¶ Mean Average Percentage Error.
\[MAPE(\mathbf{y}, \mathbf{p}) = |S| \sum_{i \in S} | \frac{y_i - p_i}{y_i} |\]Parameters: - y (array-like of shape [n_samples, ]) – ground truth.
- p (array-like of shape [n_samples, ]) – predicted labels.
Returns: z – mean average percentage error.
Return type: float
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mlens.metrics.metrics.rmse(y, p)[source]¶ Root Mean Square Error.
\[RMSE(\mathbf{y}, \mathbf{p}) = \sqrt{MSE(\mathbf{y}, \mathbf{p})},\]with
\[MSE(\mathbf{y}, \mathbf{p}) = |S| \sum_{i \in S} (y_i - p_i)^2\]Parameters: - y (array-like of shape [n_samples, ]) – ground truth.
- p (array-like of shape [n_samples, ]) – predicted labels.
Returns: z – root mean squared error.
Return type: float
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mlens.metrics.metrics.wape(y, p)[source]¶ Weighted Mean Average Percentage Error.
\[WAPE(\mathbf{y}, \mathbf{p}) = \frac{\sum_{i \in S} | y_i - p_i|}{ \sum_{i \in S} |y_i|}\]Parameters: - y (array-like of shape [n_samples, ]) – ground truth.
- p (array-like of shape [n_samples, ]) – predicted labels.
Returns: z – weighted mean average percentage error.
Return type: float