model_builder

Model hyperparameters.

ModelDecisionGraphParameters

Hyperparameters that influence the decision graph of a Xpdeep model.

The model decision graph is characterized by a set of learnt decision and can be adjusted to the use cases to provide more interpretable explanations.

Please look at the documentation Model -> Create a model for a detailed explanation of each parameter.

Parameters:

Name	Type	Description	Default
max_decision_seq_length		In [1, 10], the maximum length of a decision sequence in the graph. If None, the parameter will be estimated depending on the task and nature of the data.	required
target_homogeneity_pruning_threshold		In [0,1]. If the target homogeneity in a decision population is above this threshold, the decision is pruned. If None, the threshold will be estimated depending on the task and nature of the data.	required
population_pruning_threshold		In [0,1], decision that have a population smaller than this threshold (as a percentage of the training set) are pruned. If None, the threshold will be estimated depending on the task and nature of the data.	required
prune_step		Pruning will be performed (if possible) every "prune_step" epochs. If None, will prune at total_epochs/2 epochs.	required
target_homogeneity_weight		Coefficient that incentivizes the model to learn a decision graph with homogeneous decision. Combined with pruning, helps to build a more concise decision graph, can hinder performance if too high. If None, the coefficient will be estimated depending on the task and nature of the data.	required
discrimination_weight	float | None	Coefficient that pushes the model to learn more discriminative decisions, can hinder performance if too high. If None, the coefficient will be estimated depending on the task and nature of the data.	None
balancing_weight		Coefficient that pushes the model to learn decisions with balanced population, can hinder performance if too high.	required