# Prediction Accuracy Metrics¶

The lenskit.metrics.predict module containins prediction accuracy metrics.

## Metric Functions¶

lenskit.metrics.predict.rmse(predictions, truth, missing='error')

Compute RMSE (root mean squared error).

Parameters: predictions (pandas.Series) – the predictions truth (pandas.Series) – the ground truth ratings from data missing (string) – how to handle predictions without truth. Can be one of 'error' or 'ignore'. the root mean squared approximation error double
lenskit.metrics.predict.mae(predictions, truth, missing='error')

Compute MAE (mean absolute error).

Parameters: predictions (pandas.Series) – the predictions truth (pandas.Series) – the ground truth ratings from data missing (string) – how to handle predictions without truth. Can be one of 'error' or 'ignore'. the mean absolute approximation error double

## Working with Missing Data¶

LensKit rating predictors do not report predictions when their core model is unable to predict. For example, a nearest-neighbor recommender will not score an item if it cannot find any suitable neighbors. Following the Pandas convention, these items are given a score of NaN (when Pandas implements better missing data handling, it will use that, so use pandas.Series.isna()/pandas.Series.notna(), not the isnan versions.

However, this causes problems when computing predictive accuracy: recommenders are not being tested on the same set of items. If a recommender only scores the easy items, for example, it could do much better than a recommender that is willing to attempt more difficult items.

A good solution to this is to use a fallback predictor so that every item has a prediction. In LensKit, lenskit.algorithms.basic.Fallback implements this functionality; it wraps a sequence of recommenders, and for each item, uses the first one that generates a score.

You set it up like this:

cf = ItemItem(20)
base = Bias(damping=5)
algo = Fallback(cf, base)