Automated Feature Selection for Machine Learning Models
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Solution Overview
Problem
Current machine learning model generation methods, particularly those using Logistic Regression (LR), require extensive feature engineering and screening, consuming significant time and manpower due to the need for manual intervention in selecting features for the target model.
Innovation Solution
A method that involves acquiring sample resource features and labels, determining screening factors to filter features, and using parameters from a pre-trained LR model to obtain target model features, thereby reducing reliance on manual feature selection and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual feature engineering and screening are used to select features for the target model, then the model can be trained with carefully selected features, but significant time and manpower are consumed
Solution Approach 1:
A pre-trained Logistic Regression model is used to perform preliminary feature screening before the main model training. The screening factors from the pre-trained model identify important features in advance, reducing the manual feature engineering workload while maintaining selection quality
Solution Approach 2:
The pre-trained LR model acts as an intermediary between raw features and the target model. It processes features through automated screening factors to produce a refined feature set, eliminating the need for manual intervention in feature selection
2Reliability
If extensive feature engineering is performed to determine features for the target model, then the model input quality is improved, but the process relies heavily on manual intervention
Solution Approach 1:
The system performs automated feature screening using screening factors derived from the pre-trained LR model. The automated process selects features based on statistical measures without requiring manual intervention, while still ensuring high input quality for the target model
Solution Approach 2:
Screening factors from the pre-trained model (such as coefficients, p-values, information value) are used as automated criteria to evaluate and select features. These parameter-based metrics replace manual judgment with objective, repeatable automated selection
Data Source
AI summary
The present disclosure discloses a method, apparatus and device for generating a model and a storage medium. A method can include: acquiring sample resource features and sample labels; determining a first screening factor according to the sample resource features and the sample labels, and determining first resource features from the sample resource features according to the first screening factor; determining a second screening factor, and determining second resource features from the first resource features based on the second screening factor, and obtaining features of a target model based on the second resource features; and training a machine learning model, by taking the features of the target model as an input of the target model, and taking the sample labels corresponding to the features of the target model as an output of the target model, to obtain the trained target model.