Data-Driven Featurization for Scalable Predictive Modeling
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Solution Overview
Problem
Current predictive models face challenges in efficiently generating accurate and generic models across various domains due to the vast amount of available data, requiring significant resources and time, and struggle to identify relevant features for predicting future events.
Innovation Solution
A system and method for data-driven featurization that includes data extraction, transformation, annotation, censoring, and analysis using techniques like chi-square testing and association rule learning to produce predictive models, utilizing feedback from domain experts and converting features into binary representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional predictive modeling methods are used to analyze large amounts of data, then model accuracy can be improved, but resource consumption and time requirements increase significantly
Solution Approach 1:
The patent segments the feature selection process into multiple stages: initial feature generation from raw data, preliminary filtering using domain knowledge, and final selection through automated algorithms. This segmentation allows the system to handle large datasets efficiently by breaking down the complex task into manageable steps, improving both accuracy and efficiency.
Solution Approach 2:
The system performs preliminary actions by pre-processing data to extract potential features before the actual modeling process. Domain experts provide preliminary guidance on which features are likely to be relevant, and automated systems pre-filter vast amounts of data to identify candidate features, reducing the computational burden during model training and deployment.
2Reliability
If domain experts are involved in creating predictive models through analysis and research, then model effectiveness for specific events improves, but resource intensity and time requirements become unsustainable for scaling to thousands of events
Solution Approach 1:
The system implements feedback mechanisms where domain experts validate and refine automated feature selections. The feedback loop allows experts to review algorithm-generated features, provide corrections, and guide the system's learning process. This collaboration maintains high model effectiveness while significantly reducing the time required compared to traditional expert-only approaches.
Solution Approach 2:
The system enables self-service by allowing automated algorithms to independently identify and select relevant features from large datasets without requiring extensive manual intervention for each modeling task. Once trained on initial data with expert guidance, the system can autonomously generate predictive models for new events, dramatically reducing development time while maintaining reliability.
3Adaptability or versatility
If a predictive model is made generic to apply to a wide variety of future data sets, then adaptability improves, but the ability to provide accurate predictions for specific events may be compromised
Solution Approach 1:
The system employs dynamic feature selection that adapts to different domains and data types. Rather than using a fixed set of features, the system dynamically identifies and selects the most relevant features based on the specific characteristics of each dataset and domain. This dynamic approach allows the same generic framework to achieve high accuracy across diverse applications by adjusting its feature set to match the specific context.
Data Source
AI summary
Computer-implemented systems and methods are disclosed for automatically generating predictive models using data driven featurization. The systems and methods provide for obtaining data associated with a target event, annotating the data to identify a target event and establishing one or more limits on the data, censoring the data based on the annotations, determining features of the censored data, and analyzing the features to determine a predictive model. In some embodiments, the systems and methods further provide for converting the features into a binary representation and analyzing the binary representation to produce the predictive model.