Training Example Suggestion via Multi-Model Scoring
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Solution Overview
Problem
Existing predictive models often require large amounts of training data and struggle to identify the most useful training examples, leading to suboptimal performance, especially in scenarios with limited data for specific categories or feature spaces.
Innovation Solution
A machine learning system that receives and ranks training examples based on suggestion scores, computed by measuring performance across multiple models, using factors like ambiguity, difficulty, and sparseness scores, to identify and suggest the most useful examples for improving model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple predictive models are trained and evaluated to identify useful training examples, then model performance improvement is achieved, but computational complexity and processing time increase
Solution Approach 1:
The system segments the training data by categorizing training examples into different feature spaces and categories. By dividing the large training dataset into manageable segments based on categorical attributes, the system can efficiently identify sparse categories without processing the entire dataset at once, thus improving model performance while controlling computational complexity
Solution Approach 2:
The system applies local quality by computing suggestion scores specifically for training examples in sparse categories rather than uniformly processing all examples. The ambiguity score, difficulty score, and sparseness score are calculated locally for relevant examples, allowing the system to focus computational resources on areas that most need improvement
2Reliability
If training data quantity is increased to improve model performance, then model accuracy improves, but data collection and processing time increase
Solution Approach 1:
The system performs preliminary action by pre-computing suggestion scores, ambiguity scores, difficulty scores, and sparseness scores for training examples before actual model training. This advance preparation identifies which examples would be most beneficial, allowing operators to prioritize data collection and processing efforts on high-value examples rather than treating all examples equally
Solution Approach 2:
The system replaces the mechanical approach of uniformly collecting and processing large volumes of training data with an intelligent selection mechanism. By using machine learning models to compute suggestion scores and identify sparse categories, the system substitutes brute-force data accumulation with targeted example selection, reducing the overall data collection time while maintaining model accuracy
3Productivity
If training examples are selectively identified and ranked by suggestion scores, then training efficiency improves, but computational resources for scoring increase
Solution Approach 1:
The system applies parameter changes by computing multiple scoring parameters (suggestion score, ambiguity score, difficulty score, sparseness score) for each training example. These different parameter dimensions allow the system to evaluate training examples from multiple perspectives and make more informed selection decisions, improving training efficiency through multi-criteria evaluation
Solution Approach 2:
The system implements partial action by computing suggestion scores and related metrics for only the most relevant training examples rather than exhaustively processing every single example in the dataset. By focusing computational resources on examples that are likely to provide the most value (those in sparse categories with high ambiguity or difficulty), the system achieves good training efficiency with reduced computational overhead
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for suggesting training examples. In one aspect, a method includes receiving a plurality of training examples. A plurality of different types of predictive models are trained using the received training examples, wherein each of the predictive models implements a different machine learning technique. The performance of each trained model is measured. A suggestion score is computed for each training example according to each respective trained model, including weighting each suggestion score by the measured performance of the respective trained model. The computed suggestion scores for each training example are combined to compute an overall suggestion score for each training example, and the training examples are ranked by suggestion scores.