accuracy module¶

The surprise.accuracy module provides tools for computing accuracy metrics on a set of predictions.

Available accuracy metrics:

 rmse Compute RMSE (Root Mean Squared Error). mse Compute MSE (Mean Squared Error). mae Compute MAE (Mean Absolute Error). fcp Compute FCP (Fraction of Concordant Pairs).
surprise.accuracy.fcp(predictions, verbose=True)[source]

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
Returns

The Fraction of Concordant Pairs.

Raises

ValueError – When predictions is empty.

surprise.accuracy.mae(predictions, verbose=True)[source]

Compute MAE (Mean Absolute Error).

$\text{MAE} = \frac{1}{|\hat{R}|} \sum_{\hat{r}_{ui} \in \hat{R}}|r_{ui} - \hat{r}_{ui}|$
Parameters
Returns

The Mean Absolute Error of predictions.

Raises

ValueError – When predictions is empty.

surprise.accuracy.mse(predictions, verbose=True)[source]

Compute MSE (Mean Squared Error).

$\text{MSE} = \frac{1}{|\hat{R}|} \sum_{\hat{r}_{ui} \in \hat{R}}(r_{ui} - \hat{r}_{ui})^2.$
Parameters
Returns

The Mean Squared Error of predictions.

Raises

ValueError – When predictions is empty.

surprise.accuracy.rmse(predictions, verbose=True)[source]

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
Returns

The Root Mean Squared Error of predictions.

Raises

ValueError – When predictions is empty.