model_builder
Model hyperparameters.
Classes:
| Name | Description |
|---|---|
ModelDecisionGraphParameters |
Hyperparameters that influence the decision graph of a Xpdeep model. |
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 |
|---|---|---|---|
|
int | Literal[default]
|
This parameter defines the maximum depth of the decision graph. Increasing the value allows for more decision nodes, providing finer granularity in the decision-making process, but also adds to the complexity of the resulting graph. The depth should be set according to the desired balance between explanation detail and overall complexity. In a classification task, the default value is the logarithm of the number of classes, rounded up to the nearest integer. In a regression task, it is set to 3. |
"default"
|
|
float
|
This parameter controls the homogeneity of the target variable within graph nodes. In classification tasks, a higher value increases class purity within nodes, while in regression tasks, it reduces target variable variance. A high value, particularly greater than 1, may negatively impact the model's performance. A value of 0 disregards target variable homogeneity. By default, the value is set to 0.1. |
0.1
|
|
float
|
This parameter controls the differentiation between the left and right input features at each node. Increasing this weight enhances the distinction between the left and right descriptive features, making the separation more pronounced. A high value, particularly greater than 1, may negatively impact the model's performance. A value of 0 disregards this discriminative constraint. By default, the value is set to 0.01. |
0.01
|
|
float
|
This parameter regulates the balance between the proportions of individuals sent left and right at each node. A high value, especially above 1, can negatively affect the model's performance. A value of 0 ignores this balancing. By default, the value is set to 0. |
0.0
|
|
int | None
|
Pruning will be conducted every prune_step epochs, it varies within [1, max-epochs]. To disregard this parameter,set its value to None. This will cause the target_homogeneity_pruning_threshold and population_pruning_threshold parameters to be ignored. |
None
|
|
float | None
|
To obtain a decision graph with an optimal structure, nodes that are sufficiently homogeneous are converted into leaves (i.e., pruned). Namely, if the homogeneity of the target variable within a node exceeds this threshold, it is converted into a leaf. A value of 1 ignores this criterion. In particular, - In the classification task, homogeneity is measured by the proportion of the majority class within a node. This parameter varies within the range [max_prop, 1], where max_prop represents the proportion of the majority class in the training data. By default, the value is set to 0.9. - In the regression task, homogeneity is based on the ratio between the variance of the target variable (noted Y) in a node and the total variance of Y in the training set, namely: 1 - Var(Y/node)/Var(Y/train). This parameter varies within the range [0, 1]. By default, the value is set to 0.1. |
None
|
|
float | None
|
To obtain a decision graph with an optimal structure, nodes that involve very few individuals (i.e., not sufficiently represented) are converted into leaves. This parameter, which ranges from [0, 1], specifies the minimum proportion of individuals required in the nodes. If the proportion falls below this threshold, the node is pruned. To ignore this parameter, set its value to 0. By default, the value is set to 0.01. |
None
|