Create setting for logistics regression model with python
setLRTorch( w_decay = c(5e-04, 0.005), epochs = c(20, 50, 100), seed = NULL, class_weight = 0, autoencoder = FALSE, vae = FALSE )
w_decay | The l2 regularisation |
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epochs | The number of epochs |
seed | A seed for the model |
class_weight | The class weight used for imbalanced data: 0: Inverse ratio between positives and negatives -1: Focal loss |
autoencoder | First learn stakced autoencoder for input features, then train LR on the encoded features. |
vae | First learn stakced varational autoencoder for input features, then train LR on the encoded features. |
if (FALSE) { model.lrTorch <- setLRTorch() }