RANDOM FOREST CLASSIFIER CLASS ASSOCIATION RULE MINING
Techniques described herein relate a method for explainability for Random Forest (RF) classifiers. The method may include generating a plurality of class labels for a target variable; training a RF classifier using the plurality of class labels and a historical dataset to obtain a trained RF classifier; building a transaction database using the trained RF classifier; identifying a plurality of class association rules using the transaction database; identifying a portion of the plurality of class association rules that have minimum confidence values greater than a minimum confidence value threshold; and presenting the portion of the plurality of class association rules to an interested entity as explainability results.
1 . A method for explainability for Random Forest (RF) classifiers, the method comprising:
generating a plurality of class labels for a target variable;
training a RF classifier using the plurality of class labels and a historical dataset to obtain a trained RF classifier;
building a transaction database using the trained RF classifier;
identifying a plurality of class association rules using the transaction database;
identifying a portion of the plurality of class association rules that have minimum confidence values greater than a minimum confidence value threshold; and
presenting the portion of the plurality of class association rules to an interested entity as explainability results.
2 . The method of claim 1 , wherein generating the plurality of class labels comprises performing a clustering analysis using the historical dataset.
3 . The method of claim 1 , wherein the trained RF classifier comprises a plurality of decision trees.
4 . The method of claim 3 , wherein building the transaction database comprises:
generating, using a decision tree of the plurality of decision trees, a transaction database record comprising a class label of the plurality of class labels associated with a plurality of decision tree step results between a root of the decision tree and a decision tree leaf comprising the class label.
5 . The method of claim 1 , wherein identifying the plurality of class association rules comprises:
identifying a plurality of frequent items in the transaction database using a minimum support value; and
generating an association rule comprising a frequent item of the plurality of frequent items and a class label in the transaction database.
6 . The method of claim 5 , wherein identifying the portion of the plurality of class association rules that have the minimum confidence values greater than the minimum confidence value threshold comprises:
calculating an appearance support value for the frequent item of the plurality of frequent items;
calculating an association rule support value for the association rule;
calculating an association rule confidence value using the association rule support value and the appearance support value; and
performing a comparison of the association rule confidence value and the minimum confidence value threshold.
7 . The method of claim 6 , wherein calculating the association rule confidence value comprises dividing the appearance support value by the association rule support value.
8 . The method of claim 5 , wherein identifying the plurality of frequent items comprises:
determining a quantity of transaction records in the transaction database that comprises the frequent item of the plurality of frequent items; and
performing a comparison of the quantity with the minimum support value.
9 . The method of claim 5 , wherein calculating an association rule support value comprises determining a percentage of transaction database records in the transaction database that include the association rule.
10 . A non-transitory computer readable medium comprising computer readable program code, which when executed by a computer processor enables the computer processor to perform a method for explainability for Random Forest (RF) classifiers, the method comprising:
generating a plurality of class labels for a target variable;
training a RF classifier using the plurality of class labels and a historical dataset to obtain a trained RF classifier;
building a transaction database using the trained RF classifier;
identifying a plurality of class association rules using the transaction database;
identifying a portion of the plurality of class association rules that have minimum confidence values greater than a minimum confidence value threshold; and
presenting the portion of the plurality of class association rules to an interested entity as explainability results.
11 . The non-transitory computer readable medium of claim 10 , wherein generating the plurality of class labels comprises performing a clustering analysis using the historical dataset.
12 . The non-transitory computer readable medium of claim 10 , wherein the trained RF classifier comprises a plurality of decision trees.
13 . The non-transitory computer readable medium of claim 12 , wherein the method performed by executing the computer readable program code further comprises:
generating, using a decision tree of the plurality of decision trees, a transaction database record comprising a class label of the plurality of class labels associated with a plurality of decision tree step results between a root of the decision tree and a decision tree leaf comprising the class label.
14 . The non-transitory computer readable medium of claim 10 , wherein the method performed by executing the computer readable program code further comprises:
identifying a plurality of frequent items in the transaction database using a minimum support value; and
generating an association rule comprising a frequent item of the plurality of frequent items and a class label in the transaction database.
15 . The non-transitory computer readable medium of claim 14 , wherein identifying the portion of the plurality of class association rules that have the minimum confidence values greater than the minimum confidence value threshold comprises:
calculating an appearance support value for the frequent item of the plurality of frequent items;
calculating an association rule support value for the association rule;
calculating an association rule confidence value using the association rule support value and the appearance support value; and
performing a comparison of the association rule confidence value and the minimum confidence value threshold.
16 . The non-transitory computer readable medium of claim 15 , wherein calculating the association rule confidence value comprises dividing the appearance support value by the association rule support value.
17 . The non-transitory computer readable medium of claim 14 , wherein identifying the plurality of frequent items comprises:
determining a quantity of transaction records in the transaction database that comprises the frequent item of the plurality of frequent items; and
performing a comparison of the quantity with the minimum support value.
18 . The non-transitory computer readable medium of claim 14 , wherein calculating an association rule support value comprises determining a percentage of transaction database records in the transaction database that include the association rule.
19 . A system for explainability for Random Forest (RF) classifiers, the system comprising:
an explainability analyzer, executing on a processor comprising circuitry, and configured to:
generate a plurality of class labels for a target variable;
train a RF classifier using the plurality of class labels and a historical dataset to obtain a trained RF classifier;
build a transaction database using the trained RF classifier;
identify a plurality of class association rules using the transaction database;
identify a portion of the plurality of class association rules that have minimum confidence values greater than a minimum confidence value threshold; and
present the portion of the plurality of class association rules to an interested entity as explainability results.
20 . The system of claim 19 , wherein the explainability analyzer is further configured to:
identify a plurality of frequent items in the transaction database using a minimum support value; and
generate an association rule comprising a frequent item of the plurality of frequent items and a class label in the transaction database.