Unified Interface for Machine Learning Model Comparison
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face challenges in exploring and comparing large data sets using multiple variables across distinct machine-learning models, as they often require expertise in various techniques and different interfaces for each model.
Innovation Solution
A comparative modeling system that generates a unified graphical user interface, allowing users to select and compare machine-learning models, adjust parameters, and iteratively model data sets using multiple algorithms, facilitating the selection of suitable models and tracking input and results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users generate multiple distinct machine-learning models separately by inputting variable changes for each model, then they can explore data sets using multiple variables, but the process becomes time-consuming and requires expertise in each machine-learning technique
Solution Approach 1:
The patent combines multiple machine-learning model generation processes into a single unified interface. Users can select multiple machine-learning techniques and input variable changes once, and the system automatically generates and compares multiple distinct models across different techniques, eliminating the need to separately input variables for each model and significantly reducing the time required.
Solution Approach 2:
The patent creates a universal interface that works across multiple machine-learning techniques simultaneously. A single interface allows users to apply variable changes to multiple different machine-learning algorithms (e.g., decision trees, neural networks, SVMs) without needing separate interfaces for each technique, making the system multi-functional and eliminating the need for users to learn multiple interfaces.
2Adaptability or versatility
If users use differing interfaces for each machine-learning technique, then they can access specific model features, but the complexity of operation increases and ease of use decreases
Solution Approach 1:
The patent implements a universal graphical user interface that provides consistent access to machine-learning model features across multiple techniques. The interface presents a unified set of controls and options that work with different machine-learning algorithms, eliminating the need for users to learn and switch between multiple different interfaces while still providing access to technique-specific features through the same standardized interface.
3Adaptability or versatility
If users manually input variable changes for each distinct model, then they can customize each model, but the device complexity and operational difficulty increase
Solution Approach 1:
The patent merges the variable input mechanism into a single shared interface that serves multiple machine-learning models simultaneously. Users define variable changes once in the unified interface, and the system automatically applies these variable changes across all selected machine-learning techniques, eliminating the need for separate input mechanisms for each model and reducing overall system complexity.
Solution Approach 2:
The patent allows users to pre-configure variable changes and model parameters in the unified interface before model generation. The system stores these preliminary configurations and automatically applies them when generating multiple models, reducing the operational complexity of repeatedly inputting the same variable changes for each model.
Data Source
AI summary
In various example embodiments, a comparative modeling system is configured to receive selections of a data set, a transform scheme, and one or more machine-learning algorithms. In response to a selection of the one or more machine-learning algorithms, the comparative modeling system determines parameters within the one or more machine-learning algorithms. The comparative modeling system generates a plurality of models for the one or more machine-learning algorithms, determines comparison metric values for the plurality of models, and causes presentation of the comparison metric values for the plurality of models.