Machine Learning Model Training Automation via Problem Type Mapping
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Solution Overview
Problem
Choosing a suitable machine learning model for training sample data is challenging due to poor training results and the time-consuming process of repeated model training, which affects the training progress.
Innovation Solution
A method and device for machine learning model training that involves a problem type selection interface, a type and model relationship table, and iterative training with parameter adjustment using convolution and fully connected layers, along with Backpropagation, to determine and optimize the appropriate machine learning model for specific tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple machine learning models are available for training, then model selection flexibility is improved, but the difficulty of choosing a suitable model increases
Solution Approach 1:
The system automatically selects and trains the most suitable machine learning model based on the problem type and sample data characteristics, eliminating the need for manual model selection by users. The training device autonomously determines the optimal model from multiple available models and performs iterative training until convergence criteria are met.
Solution Approach 2:
The system pre-establishes a mapping relationship between problem types and suitable machine learning models before training begins. This preliminary configuration allows the system to quickly identify and select the appropriate model based on the input problem type, avoiding time-consuming manual selection processes.
2Measurement precision
If repeated training is performed to improve model performance, then training accuracy is improved, but training time and cost increase
Solution Approach 1:
The system implements iterative training with feedback mechanisms where training results are continuously evaluated against convergence criteria. Based on the evaluation outcomes, the system automatically adjusts training parameters and selects the best performing model, eliminating unnecessary repeated training and reducing overall training time while maintaining high accuracy.
Solution Approach 2:
The system dynamically adjusts training parameters such as learning rate, number of iterations, and model complexity based on real-time performance evaluation. This adaptive parameter adjustment allows the system to achieve optimal accuracy with minimal training iterations, avoiding time-consuming repeated training processes.
3Ease of operation
If manual model selection and training is performed, then control over the training process is improved, but the time required for training progress decreases
Solution Approach 1:
The training device autonomously performs model selection, parameter optimization, and iterative training without requiring manual intervention. The system automatically evaluates training results against convergence criteria and adjusts parameters accordingly, significantly accelerating training progress while maintaining full control through automated decision-making.
Solution Approach 2:
The system replaces manual mechanical operations with automated computational processes. Instead of requiring users to manually select models and adjust parameters, the system uses algorithmic approaches to automatically determine optimal models and training configurations, thereby increasing training productivity.
Data Source
AI summary
A machine learning model training method includes receiving a problem type to be processed, determining a machine learning model corresponding to the problem type, receiving sample data to train the determined machine learning model, analyzing training results of the determined machine learning model obtained after training is completed and displaying the training results that meet preset conditions, and providing a machine learning model corresponding to the training result that meets the preset conditions.