KNNRecommenderTrain uses the input data to generate three model tables: the weights model ('ml_weights'), the bias model table ('ml_bias') and the optional nearest items or neighbors table ('ml_itemngbrs').
DROP TABLE IF EXISTS ml_weights; DROP TABLE IF EXISTS ml_bias; DROP TABLE IF EXISTS ml_itemngbrs; SELECT iternum, rmse::VARCHAR(6) AS rmse FROM KnnRecommenderTrain ( ON (SELECT 1) PARTITION BY 1 RatingTable ('ml_ratings') UserIdColumn ('userid') ItemIdColumn ('itemid') RatingColumn ('rating') WeightModelTable ('ml_weights') BiasModelTable ('ml_bias ') NearestItemsTable ('ml_itemngbrs') K (15) MaxIterNum (20) Threshold (0.0002) LearningRate (0.001) ) ORDER BY iternum NULLS FIRST;