AutoXAI Model Search with Evolutionary NAS for Explainable ML
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Solution Overview
Problem
Conventional machine learning models rely on handpicked hyperparameters, which are arbitrary and lack explainability, making it difficult to understand and interpret the learning process.
Innovation Solution
The implementation of an explainable model search using automatic machine learning and neural architecture search techniques, which identifies features, maps feature costs, and iteratively optimizes candidates based on objectives and constraints to provide optimal explainable models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine learning models use handpicked hyperparameters, then model performance can be achieved, but the learning process becomes opaque and unexplainable
Solution Approach 1:
The system automatically performs feature selection, hyperparameter optimization, and model architecture search without requiring manual intervention. The automated machine learning pipeline selects features, determines optimal hyperparameters, and generates explainable models autonomously, transforming the previously manual process into a self-service system that maintains both performance and interpretability
Solution Approach 2:
The system incorporates feedback mechanisms where model performance metrics and explainability measures are continuously evaluated and fed back into the optimization process. This feedback loop enables the system to iteratively improve both model accuracy and explainability by adjusting features and hyperparameters based on actual performance data
2Loss of information
If manual feature selection and hyperparameter tuning are performed, then model interpretability is maintained, but the process is time-consuming and labor-intensive
Solution Approach 1:
The automated machine learning system performs feature selection, hyperparameter optimization, and model training autonomously without requiring manual intervention. The system automatically evaluates multiple features, selects optimal combinations, and tunes hyperparameters based on predefined objectives, dramatically reducing the time and labor previously required for these tasks while maintaining model interpretability through automated explanation generation
Solution Approach 2:
The system performs preliminary feature engineering, feature selection, and hyperparameter optimization before model training. By completing these time-consuming tasks automatically in advance, the system prepares all necessary components for efficient model training, eliminating the need for manual preparation and significantly reducing overall development time
3Productivity
If automated machine learning techniques are applied, then model development efficiency is improved, but the complexity of the system increases
Solution Approach 1:
The system divides the complex automated machine learning process into distinct modular segments: feature selection module, hyperparameter optimization module, model training module, and explanation generation module. Each segment handles a specific task independently, making the overall complex system more manageable and easier to implement while maintaining high automation efficiency
Solution Approach 2:
The system introduces intermediary components that bridge the gap between raw data and final models. These intermediaries include automated feature engineering layers, hyperparameter search algorithms, and explanation generation modules that translate complex model internals into human-understandable insights, simplifying the system's operational complexity while maintaining productivity
Data Source
AI summary
An exemplary model search may provide optimal explainable models based on a dataset. An exemplary embodiment may identify features from a training dataset, and may map feature costs to the identified features. The search space may be sampled to generate initial or seed candidates, which may be chosen based on one or more objectives and/or constraints. The candidates may be iteratively optimized until an exit condition is met. The optimization may be performed by an external optimizer. The external optimizer may iteratively apply constraints to the candidates to quantify a fitness level of each of the seed candidates. The fitness level may be based on the constraints and objectives. The candidates may be a set of data, or may be trained to form explainable models. The external optimizer may optimize the explainable models until the exit conditions are met.