accuracy module¶
The surprise.accuracy
module provides with tools for computing accuracy
metrics on a set of predictions.
Available accuracy metrics:
rmse |
Compute RMSE (Root Mean Squared Error). |
mae |
Compute MAE (Mean Absolute Error). |
fcp |
Compute FCP (Fraction of Concordant Pairs). |
-
surprise.accuracy.
fcp
(predictions, verbose=True)¶ Compute FCP (Fraction of Concordant Pairs).
Computed as described in paper Collaborative Filtering on Ordinal User Feedback by Koren and Sill, section 5.2.
Parameters: - predictions (
list
ofPrediction
) – A list of predictions, as returned by thetest()
method. - verbose – If True, will print computed value. Default is
True
.
Returns: The Fraction of Concordant Pairs.
Raises: ValueError
– Whenpredictions
is empty.- predictions (
-
surprise.accuracy.
mae
(predictions, verbose=True)¶ Compute MAE (Mean Absolute Error).
\[\text{MAE} = \frac{1}{|\hat{R}|} \sum_{\hat{r}_{ui} \in \hat{R}}|r_{ui} - \hat{r}_{ui}|\]Parameters: - predictions (
list
ofPrediction
) – A list of predictions, as returned by thetest()
method. - verbose – If True, will print computed value. Default is
True
.
Returns: The Mean Absolute Error of predictions.
Raises: ValueError
– Whenpredictions
is empty.- predictions (
-
surprise.accuracy.
rmse
(predictions, verbose=True)¶ Compute RMSE (Root Mean Squared Error).
\[\text{RMSE} = \sqrt{\frac{1}{|\hat{R}|} \sum_{\hat{r}_{ui} \in \hat{R}}(r_{ui} - \hat{r}_{ui})^2}.\]Parameters: - predictions (
list
ofPrediction
) – A list of predictions, as returned by thetest()
method. - verbose – If True, will print computed value. Default is
True
.
Returns: The Root Mean Squared Error of predictions.
Raises: ValueError
– Whenpredictions
is empty.- predictions (