Visualization of Training Data Impact on Machine Learning Models
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Solution Overview
Problem
The inefficiency of training neural networks due to the black-box nature of machine learning models, making it difficult for users to understand how training data impacts the model, leading to prolonged training times.
Innovation Solution
An apparatus and method that visualize the impact of training data on a machine learning model by assigning colors to identified words based on a predefined mapping, allowing users to track and understand the data flow through the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large corpus of training data is fed into the neural network to achieve desired prediction accuracy, then the model's predictive accuracy is improved, but the training time is excessively prolonged
Solution Approach 1:
The system implements feedback by continuously monitoring which words are extracted at each layer of the neural network and using this information to dynamically adjust the training process. The visualization dashboard provides real-time feedback to data scientists about data impact, enabling them to make informed decisions about data selection and model optimization, thereby reducing unnecessary training iterations.
Solution Approach 2:
The patent introduces an intermediary visualization system that sits between the training data and the neural network. This intermediary component extracts and analyzes words from training data, maps them to colors, and presents them through a dashboard. This mediator enables data scientists to understand data impact without requiring them to directly interpret complex neural network operations, thus optimizing data selection and reducing training time.
2Adaptability or versatility
If the neural network processes training data through multiple layers to generate predictions, then the model's analytical capability is improved, but the transparency of the process deteriorates
Solution Approach 1:
The patent segments the neural network into discrete layers and tracks word extraction at each layer independently. By dividing the complex processing into manageable segments and visualizing each layer's contribution separately, the system maintains analytical capability while restoring transparency. The dashboard displays which words are extracted at each layer, allowing users to trace the processing journey of training data through the network.
3Productivity
If training data is converted into vector form for neural network processing, then the model's computational efficiency is improved, but the readability of the data deteriorates
Solution Approach 1:
The visualization system acts as an intermediary that bridges the gap between vectorized data and human readability. It extracts words from the vectorized training data, maps them to meaningful color representations, and displays them through a user-friendly dashboard. This intermediary maintains computational efficiency by working with vectorized data while simultaneously providing readable visual feedback to data scientists.
Solution Approach 2:
The patent employs color changes as a visual encoding mechanism to represent words extracted from training data. Each word is mapped to a specific color, and the dashboard displays these colored representations to indicate data impact. This color-based visualization transforms abstract vector data into intuitive visual information that is easily interpretable by humans while maintaining the computational benefits of vectorized processing.
Data Source
AI summary
An example operation may include one or more of executing a machine learning model on training data, where the training data comprises a plurality of word strings, identifying words within the training data that are extracted by the machine learning model during the executing, determining a color for the machine learning model based on the identified words and a predefined mapping of words to colors, and rendering, via a user interface, a label associated with the machine learning model in the determined color for the machine learning model.