Model Quality Report
Metrics
The following metrics are computed as a result of model evaluation:
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Regression metrics:
- explained_variance_score: sklearn doc
- mean_squared_error: sklearn doc
- r2_score: sklearn doc
- mean_error: \(\text{mean}\left(y_{true}-y_{pred}\right)\)
- median_error: \(\text{med}\left(y_{true}-y_{pred}\right)\)
- mean_sign_error: \(\text{mean}\left(\text{sign}\left(y_{true}-y_{pred}\right)\right)\)
- mean_absolute_error: sklearn doc
- median_absolute_error: \(\text{med}\left|y_{true}-y_{pred}\right|\)
- mean_percentage_error: \(\text{mean}\left(\left(y_{true}-y_{pred}\right)/y_{true}\right)\)
- median_percentage_error: \(\text{med}\left(\left(y_{true}-y_{pred}\right)/y_{true}\right)\)
- mean_absolute_percentage_error: \(\text{mean}\left|\left(y_{true}-y_{pred}\right)/y_{true}\right|\)
- median_absolute_percentage_error: \(\text{med}\left|\left(y_{true}-y_{pred}\right)/y_{true}\right|\)
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Cumulative metrics:
- mean of signs of cumulative error: \(\text{mean}\left(\text{sign}\left(\tilde{y}_{true}-\tilde{y}_{pred}\right)\right)\)
- mean absolute cumulative error: \(\text{mean}\left|\tilde{y}_{true}-\tilde{y}_{pred}\right|\)
- median absolute cumulative error: \(\text{med}\left|\tilde{y}_{true}-\tilde{y}_{pred}\right|\)
- mean absolute percentage cumulative error: \(\text{mean}\left|\left(\tilde{y}_{true}-\tilde{y}_{pred}\right)/\tilde{y}_{true}\right|\)
- median absolute percentage cumulative error: \(\text{med}\left|\left(\tilde{y}_{true}-\tilde{y}_{pred}\right)/\tilde{y}_{true}\right|\)
where \(\tilde{y}=(\tilde{y}^t)_{t=1,\ldots,T}\) is the vector of cumulative values of \(y_t\). To be precise, \(\tilde{y}^t=\overset{t}{\underset{s=1}{\sum}} y_s\).
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Classification metrics:
- accuracy: sklearn doc
- precision: sklearn doc
- recall: sklearn doc