Weighted Accuracy Evaluation for Predictive Model Selection
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Solution Overview
Problem
Existing predictive models are evaluated solely on accuracy, neglecting the utility of their decisions, which can lead to suboptimal performance in real-world applications where the value of predictions varies based on their outcomes.
Innovation Solution
Implementing a method to train and evaluate predictive models using weighted accuracy, where weights are assigned to different answer categories to reflect the relative importance of correct versus incorrect predictions, allowing for the selection of models that maximize utility based on specified criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If predictive models are evaluated solely on accuracy, then the evaluation process is simple, but the utility of decisions is lost and model performance in real-world applications becomes suboptimal
Solution Approach 1:
The patent introduces weights as a new parameter to transform the evaluation metric from simple accuracy to weighted accuracy. This parameter change allows the system to account for the varying utility of different prediction outcomes while maintaining a systematic evaluation framework. The weights are applied to different answer categories based on their relative importance, converting a binary accuracy measure into a nuanced utility-based metric.
Solution Approach 2:
The patent introduces weights as an intermediary element that mediates between the model's predictions and the final evaluation. These weights serve as a bridge that translates the varying utility of different outcomes into a unified evaluation metric. The weights are calculated based on the distribution of answer categories and the relative importance of correct versus incorrect predictions, allowing the system to capture decision utility without directly modeling complex decision scenarios.
2Reliability
If weights are assigned to different answer categories to reflect relative importance, then the utility of predictions is maximized, but the complexity of model evaluation increases
Solution Approach 1:
The patent manages complexity by systematically defining weight parameters based on the distribution of answer categories. Rather than requiring manual assignment of weights for each category, the system calculates weights automatically from the training data distribution and the specified relative importance of correct versus incorrect predictions. This parameter-based approach maintains reliability while controlling evaluation complexity through automated computation.
Solution Approach 2:
The system performs self-service by automatically calculating the weights based on the training data and the specified utility criteria. The weight calculation process is embedded within the evaluation framework, allowing the system to determine appropriate weights without external intervention for each evaluation scenario. This self-service mechanism reduces the operational complexity of implementing weighted evaluation while maintaining the ability to reflect utility differences.
3Adaptability or versatility
If multiple types of predictive models are trained and evaluated, then the best model for specific utility criteria can be selected, but the training time and computational resources increase
Solution Approach 1:
The patent applies partial action by evaluating models based on weighted accuracy rather than exhaustively evaluating all possible performance metrics. The system trains multiple model types but focuses evaluation on the weighted accuracy metric that directly reflects utility. This partial evaluation approach allows selection of the best model for specific utility criteria without the need to compute and compare all possible performance measures, reducing evaluation time while maintaining adaptability.
Solution Approach 2:
The patent uses parameter changes to streamline the evaluation process by focusing on the weighted accuracy parameter rather than multiple performance metrics. By transforming the evaluation focus to this single composite parameter that incorporates utility weights, the system can efficiently compare multiple model types and select the best performing model without extensive computational analysis of multiple separate metrics.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a plurality of different types of predictive models using training data, wherein each of the predictive models implements a different machine learning technique. One or more weights are obtained wherein each weight is associated with an answer category in the plurality of examples. A weighted accuracy is calculated for each of the predictive models using the one or more weights.