Alternative names (1)
hasHyperparameterRandom triples
| Subject | Object |
|---|---|
| gptkb:Random_Forest_Ensemble | minimum samples per leaf |
| gptkb:k-NN | k (number of neighbors) |
| gptkb:Lasso_Regression | Regularization strength (lambda) |
| gptkb:Support_Vector_Regression | C |
| gptkb:Soft_Actor-Critic_(SAC) | learning rate |
| gptkb:Random_Forest_Ensemble | maximum tree depth |
| gptkb:Random_Forests | minimum samples per leaf |
| gptkb:LDA | number of topics |
| gptkb:Soft_Actor-Critic_(SAC) | temperature parameter (alpha) |
| gptkb:GradientBoostingRegressor | n_estimators |
| gptkb:lasso_regression | lambda (regularization parameter) |
| gptkb:Soft_Actor-Critic_(SAC) | target smoothing coefficient (tau) |
| gptkb:Skip-gram_model | negative sampling |
| gptkb:StochasticWeightAveraging | LearningRate |
| gptkb:RMSprop | epsilon |
| gptkb:Elastic_Net | l1_ratio |
| gptkb:Multinomial_Naive_Bayes | fit_prior |
| gptkb:Random_Forest | number of trees |
| gptkb:GradientBoostingRegressor | learning_rate |
| gptkb:Random_Forest | max features |