# evaluate module¶

The evaluate module defines the evaluate() function.

surprise.evaluate.evaluate(algo, data, measures=[u'rmse', u'mae'], with_dump=False, dump_dir=None, verbose=1)[source]

Evaluate the performance of the algorithm on given data.

Depending on the nature of the data parameter, it may or may not perform cross validation.

Parameters: algo (AlgoBase) – The algorithm to evaluate. data (Dataset) – The dataset on which to evaluate the algorithm. measures (list of string) – The performance measures to compute. Allowed names are function names as defined in the accuracy module. Default is ['rmse', 'mae']. with_dump (bool) – If True, the predictions, the trainsets and the algorithm parameters will be dumped for later further analysis at each fold (see User Guide). The file names will be set as: '--'. Default is False. dump_dir (str) – The directory where to dump to files. Default is '~/.surprise_data/dumps/'. verbose (int) – Level of verbosity. If 0, nothing is printed. If 1 (default), accuracy measures for each folds are printed, with a final summary. If 2, every prediction is printed. A dictionary containing measures as keys and lists as values. Each list contains one entry per fold.