Grid search for finding suitable hyperparameters
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static double | FindMinimum (string evaluation_measure, string hyperparameter_name, double[] hyperparameter_values, RatingPredictor recommender, ISplit< IRatings > split) |
| Find the the parameters resulting in the minimal results for a given evaluation measure (1D) More...
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static double | FindMinimum (string evaluation_measure, string hp_name1, string hp_name2, double[] hp_values1, double[] hp_values2, RatingPredictor recommender, ISplit< IRatings > split) |
| Find the the parameters resulting in the minimal results for a given evaluation measure (2D) More...
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static double | FindMinimum (string evaluation_measure, string hyperparameter_name, double[] hyperparameter_values, RatingPrediction.RatingPredictor recommender, uint k) |
| Find the the parameters resulting in the minimal results for a given evaluation measure using k-fold cross-validation More...
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static double | FindMinimumExponential (string evaluation_measure, string hp_name1, string hp_name2, double[] hp_values1, double[] hp_values2, double basis, RatingPrediction.RatingPredictor recommender, ISplit< IRatings > split) |
| Find the the parameters resulting in the minimal results for a given evaluation measure (2D) More...
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static double | FindMinimumExponential (string evaluation_measure, string hp_name, double[] hp_values, double basis, RatingPrediction.RatingPredictor recommender, ISplit< IRatings > split) |
| Find the the parameters resulting in the minimal results for a given evaluation measure (1D) More...
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Grid search for finding suitable hyperparameters
static double FindMinimum |
( |
string |
evaluation_measure, |
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string |
hyperparameter_name, |
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|
double[] |
hyperparameter_values, |
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RatingPredictor |
recommender, |
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|
ISplit< IRatings > |
split |
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) |
| |
|
inlinestatic |
Find the the parameters resulting in the minimal results for a given evaluation measure (1D)
The recommender will be set to the best parameter value after calling this method.
- Parameters
-
evaluation_measure | the name of the evaluation measure |
hyperparameter_name | the name of the hyperparameter to optimize |
hyperparameter_values | the values of the hyperparameter to try out |
recommender | the recommender |
split | the dataset split to use |
- Returns
- the best (lowest) average value for the hyperparameter
static double FindMinimum |
( |
string |
evaluation_measure, |
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|
string |
hp_name1, |
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string |
hp_name2, |
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double[] |
hp_values1, |
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double[] |
hp_values2, |
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|
RatingPredictor |
recommender, |
|
|
ISplit< IRatings > |
split |
|
) |
| |
|
inlinestatic |
Find the the parameters resulting in the minimal results for a given evaluation measure (2D)
The recommender will be set to the best parameter value after calling this method.
- Parameters
-
evaluation_measure | the name of the evaluation measure |
hp_name1 | the name of the first hyperparameter to optimize |
hp_values1 | the values of the first hyperparameter to try out |
hp_name2 | the name of the second hyperparameter to optimize |
hp_values2 | the values of the second hyperparameter to try out |
recommender | the recommender |
split | the dataset split to use |
- Returns
- the best (lowest) average value for the hyperparameter
static double FindMinimum |
( |
string |
evaluation_measure, |
|
|
string |
hyperparameter_name, |
|
|
double[] |
hyperparameter_values, |
|
|
RatingPrediction.RatingPredictor |
recommender, |
|
|
uint |
k |
|
) |
| |
|
inlinestatic |
Find the the parameters resulting in the minimal results for a given evaluation measure using k-fold cross-validation
The recommender will be set to the best parameter value after calling this method.
- Parameters
-
evaluation_measure | the name of the evaluation measure |
hyperparameter_name | the name of the hyperparameter to optimize |
hyperparameter_values | the values of the hyperparameter to try out |
recommender | the recommender |
k | the number of folds to be used for cross-validation |
- Returns
- the best (lowest) average value for the hyperparameter
Find the the parameters resulting in the minimal results for a given evaluation measure (2D)
The recommender will be set to the best parameter value after calling this method.
- Parameters
-
evaluation_measure | the name of the evaluation measure |
hp_name1 | the name of the first hyperparameter to optimize |
hp_values1 | the logarithm values of the first hyperparameter to try out |
hp_name2 | the name of the second hyperparameter to optimize |
hp_values2 | the logarithm values of the second hyperparameter to try out |
basis | the basis to use for the logarithms |
recommender | the recommender |
split | the dataset split to use |
- Returns
- the best (lowest) average value for the hyperparameter
Find the the parameters resulting in the minimal results for a given evaluation measure (1D)
The recommender will be set to the best parameter value after calling this method.
- Parameters
-
evaluation_measure | the name of the evaluation measure |
hp_name | the name of the hyperparameter to optimize |
hp_values | the logarithms of the values of the hyperparameter to try out |
basis | the basis to use for the logarithms |
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: