Machine Learning Model Builder for Real-Time Lead Distribution
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Solution Overview
Problem
Existing systems face challenges in delivering predictive analytics in real-time environments, where decisions must be made quickly, and struggle with low predictive power when trained on small data sets.
Innovation Solution
The use of Generalized Linear Models (GLMs) and Bayesian methodologies for building machine learning models, which allow for real-time predictions and improve predictive power by incorporating prior assumptions, even with limited data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning models are used for real-time lead distribution predictions, then the system can process data quickly, but the predictive power is low when trained on small data sets
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing destination statistics (conversion rates, acceptance rates, payment rates) before real-time lead processing. These pre-computed statistics serve as prior knowledge that enhances predictive power without requiring large amounts of real-time training data. The model builder pre-processes historical data to create destination profiles that are then used during real-time operations.
Solution Approach 2:
The system creates simplified copies of complex destination behaviors through statistical representations. Instead of training complex machine learning models on small datasets, the system copies essential destination characteristics into pre-computed statistics (e.g., average conversion rate, average acceptance rate) that can be quickly applied during real-time lead distribution decisions.
2Reliability
If multiple machine learning models are built and tested to improve prediction accuracy, then the predictive power increases, but the computational time and complexity increase
Solution Approach 1:
The system performs model building and accuracy testing in advance during an offline model building phase. Multiple models are constructed and evaluated against held-out test data before deployment. The model with the highest accuracy is selected and stored for real-time use, eliminating the need to perform these time-consuming operations during real-time lead processing.
Solution Approach 2:
The system segments the model development process into distinct phases: offline model building and selection, and online real-time prediction. During the offline phase, multiple models are built and tested to identify the best performer. During the online phase, only the selected model is used for rapid real-time predictions, separating the computationally intensive model selection from the time-sensitive prediction task.
3Reliability
If the system processes and analyzes all incoming lead data in real-time to maximize profit, then the predictive accuracy improves, but the computational resources required increase
Solution Approach 1:
The system uses pre-computed destination statistics as simplified copies of complex destination evaluation processes. Instead of performing full machine learning inference for each destination during real-time processing, the system applies pre-calculated statistical metrics (conversion rates, acceptance rates) that require minimal computational resources while maintaining lead selection accuracy.
Solution Approach 2:
The system performs comprehensive data analysis and model training in advance during offline processing. Historical lead and destination data are analyzed to build predictive models and calculate destination statistics before real-time operations begin. This preliminary analysis shifts computational resource consumption from the real-time processing phase to the offline model building phase, enabling efficient real-time lead distribution decisions.
Data Source
AI summary
Systems and methods for automatically building a machine learning model are disclosed. A plurality of variables is displayed via a graphical user interface (GUI). A target variable and a first independent variable are identified from the plurality of variables. A parameter associated with the machine learning model is identified. Collected data is received via the GUI. A first machine learning model is built using as inputs, the parameter and the collected data associated with the first independent variable and the target variable. A change is made to at least a portion of the inputs used to build the first machine learning model. A second machine learning model is built based on the change. A prediction accuracy of the first machine learning model is compared to the prediction accuracy of the second machine learning model. Either the first or second machine learning model is selected based on the prediction accuracy.