Cross Validation Tool

gorse test [model] [flags]

Flags

Stage Flag Description
Task –top int evaluate the model in top N ranking (default 10)
Loaders –load-builtin string using data from built-in
–load-csv string using data from a CSV file
–csv-header header for the CSV file
–csv-sep string separator for the CSV file (default “t”)
Splitters –split-fold int split data by k fold (default 5)
Parameters –set-alpha float alpha value, depends on context
–set-init-high float upper bound of uniform initial parameters
–set-init-low float lower bound of uniform initial parameters
–set-init-mean float mean of gaussian initial parameters
–set-init-std float standard deviation of gaussian initial parameters
–set-item-clusters int number of item clusters
–set-k string number of neighbors
–set-lr float learning rate
–set-mink string least number of neighbors
–set-n-epochs int number of epochs
–set-n-factors int number of factors
–set-optimizer string set optimizer for optimization (sgd/bpr)
–set-random-state int random state (seed)
–set-reg float regularization strength
–set-shrinkage int shrinkage of similarity
–set-similarity string similarity metrics
–set-type string type for KNN
–set-use-bias using bias
–set-user-based user based if true. otherwise item based
–set-user-clusters int number of user clusters
Evaluators –eval-mae evaluating the model by MAE
–eval-map evaluating the model by MAP@N
–eval-mrr evaluating the model by MRR@N
–eval-ndcg evaluating the model by NDCG@N
–eval-precision evaluating the model by Precision@N
–eval-recall evaluating the model by Recall@N
–eval-rmse evaluating the model by RMSE

Example

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