tfdf.keras.AdvancedArguments

Advanced control of the model that most users won't need to use.

infer_prediction_signature Instantiate the model graph after training. This allows the model to be saved without specifying an input signature and without calling "predict", "evaluate". Disabling this logic can be useful in two situations: (1) When the exported signature is different from the one used during training, (2) When using a fixed-shape pre-processing that consume 1 dimensional tensors (as keras will automatically expend its shape to rank 2). For example, when using tf.Transform.
yggdrasil_training_config Yggdrasil Decision Forests training configuration. Expose a few extra hyper-parameters. yggdrasil_deployment_config: Configuration of the computing resources used to train the model e.g. number of threads. Does not impact the model quality.
fail_on_non_keras_compatible_feature_name If true (default), training will fail if one of the feature name is not compatible with part of the Keras API. If false, a warning will be generated instead.
predict_single_probability_for_binary_classification Only used for binary classification. If true (default), the prediction of a binary class model is a tensor of shape [None, 1] containing the probability of the positive class (value=1). If false, the prediction of a binary class model is a tensor of shape [None, num_classes=2] containing the probability of the complementary classes.
metadata_framework Metadata describing the framework used to train the model.
metadata_owner Metadata describing who trained the model.
populate_history_with_yggdrasil_logs If false (default) and if a validation dataset is provided, populate the model's history with the final validation evaluation computed by the Keras metric (i.e. one evaluation). If true or if no validation dataset is provided, populate the model's history with the yggdrasil training logs. The yggdrasil training logs contains more metrics, but those might not be comparable with other non TF-DF models.