feature_importance: Importance of individual features
In pievos101/DFNET: Network-guided greedy decision forest for feature subset selection
feature_importance R Documentation
Importance of individual features
Description
Calculates the average importance of features over multiple decision
trees.
Usage
feature_importance(forest, features, sep = "$")
Arguments
forest
The trained DFNET.forest.
features
matrix or 3D array. The features on which it was trained.
sep
string.
Value
A matrix of importance scores with one row per column in
features and one column per matrix (in a 3D array).
See Also
Other metrics:
edge_importance(),
module_importance(),
unique_module_importance()
pievos101/DFNET documentation built on Dec. 1, 2022, 3:44 p.m.
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| feature_importance | R Documentation |
Importance of individual features
Description
Calculates the average importance of features over multiple decision trees.
Usage
feature_importance(forest, features, sep = "$")
Arguments
forest |
The trained |
features |
matrix or 3D array. The features on which it was trained. |
sep |
string. |
Value
A matrix of importance scores with one row per column in
features and one column per matrix (in a 3D array).
See Also
Other metrics:
edge_importance(),
module_importance(),
unique_module_importance()
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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