Outlier Detection for Explainable Storage Array Prediction
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Solution Overview
Problem
Machine learning models lack transparency, making it difficult to understand their decision-making processes, which hinders their adoption and trustworthiness in real-world applications.
Innovation Solution
The implementation of an outlier detection mechanism within machine learning models, specifically a random forest framework, to quantify the difference between new observations and training data, providing a diversity score that enhances transparency and confidence in predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to make predictions, then prediction accuracy is improved, but transparency and understandability of the decision-making process deteriorates
Solution Approach 1:
The patent introduces an intermediary mechanism (outlier detection module) that sits between the input data and the machine learning model's decision-making process. This module detects outliers in the input data and provides explanations for predictions by identifying which features contribute most to the prediction outcome, thereby maintaining prediction accuracy while improving transparency through a mediating layer that bridges the gap between complex model operations and human-understandable explanations
Solution Approach 2:
The patent implements a feedback mechanism where the model's predictions are analyzed and explained by identifying outlier features and their contributions. The explanation system provides feedback about which features drove the prediction and how much they contributed, allowing users to understand the decision-making process while maintaining the model's predictive accuracy through continuous monitoring and explanation generation
2Productivity
If traditional machine learning models are deployed, then productivity is improved, but trust and confidence in predictions deteriorates due to lack of explainability
Solution Approach 1:
The patent applies preliminary action by detecting outliers and preparing explanations before final predictions are made. The outlier detection mechanism identifies unusual patterns in input data ahead of time, and the explanation system pre-calculates feature contributions and anomaly scores, so that when predictions are generated, they come with ready-made explanations that build trust without delaying the prediction process
Solution Approach 2:
The patent uses a metaphorical 'color change' approach where different levels of prediction confidence and outlier detection results are visually represented through scoring mechanisms. High-confidence predictions with normal input data receive different anomaly scores than low-confidence predictions or those with outlier inputs, providing intuitive visual cues about prediction reliability and building user trust through transparent confidence indicators
Data Source
AI summary
An outlier detection mechanism is disclosed that improves transparency and explainability in machine learning models. The outlier detection mechanism can quantify, at prediction time, how a new observation differs from training observations. The outlier detection mechanism can also provide a way to aggregate outputs from decision trees by weighting the outputs of the decision trees based on their explainability.