Lag Feature for Machine Learning Model Testing
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Solution Overview
Problem
Machine learning models are not adequately tested for real-world conditions due to the absence of simulated lag in historical data records, which can lead to suboptimal performance when deployed, as they do not account for delays from data availability, API calls, and feature generation time.
Innovation Solution
Introducing a 'lag feature' into machine learning model test procedures that restricts data record queries to predetermined times prior to the test, simulating real-world delays and allowing for evaluation of model performance under various lag conditions, enabling developers to adjust the model before deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If historical data records are used without lag simulation, then model training is faster and easier, but model performance in real-world deployment degrades due to unaccounted delays
Solution Approach 1:
The patent applies preliminary action by introducing lag features during the model training phase that simulate real-world data delays. This allows the model to learn from data that reflects actual production conditions, including the time lags between data generation, data availability, and model inference. By preparing the model with these simulated lag conditions beforehand, the model becomes more reliable when deployed without requiring extensive real-time testing later.
2Reliability
If comprehensive data records are used in testing, then model evaluation is more robust, but the test environment differs from production environment due to lag discrepancies
Solution Approach 1:
The patent applies local quality by making the data records locally adapted to reflect production environment characteristics. Specifically, the lag feature introduces targeted delays in specific data streams (e.g., transaction data, account data) to match the actual lag patterns observed in production. This allows the test environment to maintain comprehensive data for robust evaluation while simultaneously adapting the temporal characteristics of individual data sources to mirror production conditions, thereby improving environment compatibility.
3Reliability
If lag is introduced in test procedures, then real-world performance is better simulated, but data query complexity increases due to predetermined time restrictions
Solution Approach 1:
The patent applies the intermediary principle by introducing a lag feature as a mediating component between the data source and the model input. This lag feature acts as an intermediary layer that systematically introduces predetermined time delays to data records. Rather than directly complicating the data query process, the lag feature provides a structured, configurable mechanism that manages temporal discrepancies. The intermediary handles the complexity of time management centrally, making it easier to control and adjust lag parameters without fundamentally redesigning the entire data query architecture.
Data Source
AI summary
A method of analyzing the effectiveness of a machine learning model may include executing a test procedure of a machine learning model comprising a plurality of input features. The machine learning model may include a lag feature associated with one of the plurality of input features. Executing the test procedure may include querying one or more data sources for historical data records associated with the input features. The lag feature may restrict a query for historical data records from at least one of the data sources to a predetermined time prior to a time of the test procedure of the machine learning model. Executing the test procedure may include receiving the historical data records from the one or more data sources at the machine learning model. Executing the test procedure may include generating an output of the machine learning model.