Nealder-Mead algorithm for finding suitable hyperparameters
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| static double | FindMinimum (string error_measure, RatingPredictor recommender) |
| | Find best hyperparameter (according to an error measure) using Nelder-Mead search More...
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| static double | FindMinimum (string evaluation_measure, IList< string > hp_names, IList< DenseVector > initial_hp_values, RatingPredictor recommender, ISplit< IRatings > split) |
| | Find the the parameters resulting in the minimal results for a given evaluation measure More...
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Nealder-Mead algorithm for finding suitable hyperparameters
| static double FindMinimum |
( |
string |
error_measure, |
|
|
RatingPredictor |
recommender |
|
) |
| |
|
inlinestatic |
Find best hyperparameter (according to an error measure) using Nelder-Mead search
- Parameters
-
| error_measure | an error measure (lower is better) |
| recommender | a rating predictor (will be set to best hyperparameter combination) |
- Returns
- the estimated error of the best hyperparameter combination
| static double FindMinimum |
( |
string |
evaluation_measure, |
|
|
IList< string > |
hp_names, |
|
|
IList< DenseVector > |
initial_hp_values, |
|
|
RatingPredictor |
recommender, |
|
|
ISplit< IRatings > |
split |
|
) |
| |
|
inlinestatic |
Find the the parameters resulting in the minimal results for a given evaluation measure
The recommender will be set to the best parameter value after calling this method.
- Parameters
-
| evaluation_measure | the name of the evaluation measure |
| hp_names | the names of the hyperparameters to optimize |
| initial_hp_values | the values of the hyperparameters to try out first |
| recommender | the recommender |
| split | the dataset split to use |
- Returns
- the best (lowest) average value for the hyperparameter
The documentation for this class was generated from the following file:
