Classification for Asymmetric Error Costs

A non-transitory computer readable storage medium comprising instructions configured to be executed by a processor, the instructions when executed by the processor causing the processor to:

• access an external data entry comprising a set of attributes relating to fraudulent behavior in a web application;
• access a trained model that is a random forest classifier (RFC) comprising a plurality of decision trees;
• apply the trained model to the external data entry to perform an assignment to a classification label estimate for the external data entry by:
• generating a plurality of label estimates for the external data entry from each of the plurality of decision trees;
• and
• assigning the classification label estimate based on a function of the plurality of label estimates, wherein the classification label estimate relates to whether a user of the web application is engaging in fraudulent behavior, wherein at least one node of each of the decision trees of the trained model represents a condition determined using an impurity function that comprises an asymmetry biased toward determining that the condition is not related to fraudulent behavior in the web application;
• and
• store the classification label estimate in association with the external data entry in a data store.

A system comprising:

• a processor configured to execute instructions;
• a computer-readable medium containing instructions for execution on the processor, the instructions causing the processor to perform steps of:
• accessing an external data entry comprising a set of attributes relating to fraudulent behavior in a web application;
• accessing a trained model that is a random forest classifier (RFC) comprising a plurality of decision trees;
• applying the trained model to the external data entry to perform an assignment to a classification label estimate for the external data entry by:
• generating a plurality of label estimates for the external data entry from each of the plurality of decision trees;
• and
• assigning the classification label estimate based on a function of the plurality of label estimates, wherein the classification label estimate relates to whether a user of the web application is engaging in fraudulent behavior, wherein at least one node of each of the decision trees of the trained model represents a condition determined using an impurity function that comprises an asymmetry biased toward determining that the condition is not related to fraudulent behavior in the web application;
• and
• storing the classification label estimate in association with the external data entry in a data store.