Feature Evaluation Model for Machine Learning
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Solution Overview
Problem
Conventional machine learning models are inflexible and resource-intensive when modifying input features, requiring extensive re-training and substantial computational resources, making it costly and time-consuming to evaluate the impact of new features on model performance.
Innovation Solution
A feature evaluation system that uses an evaluation model with a simpler structure, such as a gradient boosting tree, to assess the performance of new features by combining the output of the existing machine learning model with the new features, reducing the need for re-training and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine learning models are used to evaluate new features, then accurate predictions can be maintained, but extensive re-training and substantial computational resources are required
Solution Approach 1:
The evaluation system segments the feature evaluation process into two independent parts: (1) the existing trained machine learning model that processes original features, and (2) a separate evaluation module that assesses new features by comparing model outputs with and without the new features. This segmentation allows accurate predictions to be maintained while avoiding extensive re-training, as the evaluation can be performed on the existing model structure.
Solution Approach 2:
The patent introduces an intermediary evaluation mechanism that acts as a mediator between the existing machine learning model and the new features. Instead of directly re-training the model with new features (which requires substantial resources), the intermediary evaluation process assesses feature impact by running comparative analyses on the existing model, thereby maintaining prediction accuracy while improving feature evaluation efficiency.
2Adaptability or versatility
If the machine learning model is re-trained to incorporate new features, then the model can utilize the new features for accurate predictions, but the process is time-consuming and resource-intensive
Solution Approach 1:
The evaluation system performs preliminary assessment of new features before actual model re-training. By evaluating the potential impact of new features using the existing model structure and comparing outputs, the system determines whether re-training is worthwhile. This preliminary action saves time by avoiding unnecessary re-training cycles and allowing the model to adapt to new features only when the evaluation shows beneficial impact.
Solution Approach 2:
The patent implements a dynamic feature evaluation approach where the model's adaptability to new features is assessed through a flexible evaluation process rather than rigid re-training. The evaluation system can dynamically determine which features to incorporate based on their measured impact, allowing the model to adapt selectively to new features without committing to full re-training cycles, thereby reducing time loss while maintaining versatility.
3Reliability
If new features are added to the machine learning model, then prediction capabilities can be enhanced, but the cost and resource expenditure increase significantly
Solution Approach 1:
The evaluation system applies local quality assessment by evaluating the impact of individual new features or small groups of features rather than assessing all possible features globally. This allows the system to identify and incorporate only those specific features that provide meaningful improvement to prediction accuracy, avoiding unnecessary increases in model complexity while maintaining enhanced prediction capabilities where beneficial.
Solution Approach 2:
The patent utilizes parameter changes in the evaluation process by measuring model performance metrics (such as accuracy, precision, recall) before and after incorporating new features. By monitoring these parameter changes, the system can determine whether the added complexity of new features justifies the improvement in prediction accuracy, thereby controlling model complexity while allowing selective enhancement of prediction capabilities.
Data Source
AI summary
Methods and systems are presented for evaluating the effects of different input features on a machine learning model. The machine learning model is configured to perform a task based on a first set of features. When a second set of features becomes available for performing the task, an evaluation model is generated for evaluating the effect of including the second set of features as input features for the machine learning model to perform the task. The evaluation model is configured to accept inputs corresponding to an output from the machine learning model and the second set of features. The performance in performing the task by the evaluation model is determined and compared against the performance of the machine learning model. Based on a performance gain of the evaluation model over the machine learning model, the machine learning model is modified to incorporate the second set of features as input features.