Alternative names (1)
hasHyperparameterRandom triples
| Subject | Object |
|---|---|
| gptkb:Soft_Actor-Critic_(SAC) | target smoothing coefficient (tau) |
| gptkb:Gradient_Boosted_Trees | Subsample Ratio |
| gptkb:Gradient_Boosted_Trees | Learning Rate |
| gptkb:LASSO | regularization parameter (lambda) |
| gptkb:Random_Forest_Ensemble | maximum tree depth |
| gptkb:Soft_Actor-Critic_(SAC) | learning rate |
| gptkb:Elastic_Net_Regression | l1_ratio |
| gptkb:LDA | alpha |
| gptkb:Boosted_Trees | Number of Trees |
| gptkb:Skip-gram_model | embedding dimension |
| gptkb:Soft_Actor-Critic_(SAC) | temperature parameter (alpha) |
| gptkb:Boosted_Trees | Subsample Ratio |
| gptkb:Support_Vector_Regression | gamma |
| gptkb:Elastic_Net | l1_ratio |
| gptkb:Random_Forests | maximum tree depth |
| gptkb:Skip-gram_model | window size |
| gptkb:StochasticWeightAveraging | LearningRate |
| gptkb:Random_Forest | min samples split |
| gptkb:Soft_Actor-Critic_(SAC) | discount factor (gamma) |
| gptkb:Lasso_Regression | Regularization strength (lambda) |