Rating¶
Elliot integrates the following ratings-based error metrics.
Summary¶
|
Mean Absolute Error |
|
Mean Squared Error |
|
Root Mean Squared Error |
MAE¶
-
class
elliot.evaluation.metrics.rating.mae.mae.MAE(recommendations, config, params, eval_objects)[source]¶ Bases:
elliot.evaluation.metrics.base_metric.BaseMetricMean Absolute Error
This class represents the implementation of the Mean Absolute Error recommendation metric.
For further details, please refer to the link
\[\mathrm{MAE}=\frac{1}{|{T}|} \sum_{(u, i) \in {T}}\left|\hat{r}_{u i}-r_{u i}\right|\]\(T\) is the test set, \(\hat{r}_{u i}\) is the score predicted by the model,
To compute the metric, add it to the config file adopting the following pattern:
simple_metrics: [MAE]
MSE¶
-
class
elliot.evaluation.metrics.rating.mse.mse.MSE(recommendations, config, params, eval_objects)[source]¶ Bases:
elliot.evaluation.metrics.base_metric.BaseMetricMean Squared Error
This class represents the implementation of the Mean Squared Error recommendation metric.
For further details, please refer to the link
\[\mathrm{MSE} = \frac{1}{|{T}|} \sum_{(u, i) \in {T}}(\hat{r}_{u i}-r_{u i})^{2}\]\(T\) is the test set, \(\hat{r}_{u i}\) is the score predicted by the model
\(r_{u i}\) the actual score of the test set.
To compute the metric, add it to the config file adopting the following pattern:
simple_metrics: [MSE]
RMSE¶
-
class
elliot.evaluation.metrics.rating.rmse.rmse.RMSE(recommendations, config, params, eval_objects)[source]¶ Bases:
elliot.evaluation.metrics.base_metric.BaseMetricRoot Mean Squared Error
This class represents the implementation of the Root Mean Squared Error recommendation metric.
For further details, please refer to the link
\[\mathrm{RMSE} = \sqrt{\frac{1}{|{T}|} \sum_{(u, i) \in {T}}(\hat{r}_{u i}-r_{u i})^{2}}\]\(T\) is the test set, \(\hat{r}_{u i}\) is the score predicted by the model
\(r_{u i}\) the actual score of the test set.
To compute the metric, add it to the config file adopting the following pattern:
simple_metrics: [RMSE]