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Explaining the performance of black box regression models
Ines Areosa,
Luis Torgo
Dentistry
科研成果
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图书/报告稿件的类型
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会议稿件
3
引用 （Scopus）
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探究 'Explaining the performance of black box regression models' 的科研主题。它们共同构成独一无二的指纹。
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Keyphrases
Regression Model
100%
Black Box
100%
Bounding Box Regression
100%
Predictor Variables
66%
Comparative Analysis
33%
Machine Learning
33%
Performance Prediction
33%
Expected Error
33%
Visual Display
33%
Multiple Models
33%
Expected Risk
33%
Machine Data
33%
Case-based Analysis
33%
Prediction Error
33%
Data Mining Model
33%
Mathematics
Regression Model
100%
Black Box
100%
End User
66%
Predictor Variable
66%
Data Mining
33%
Prediction Error
33%
Predictive Performance
33%
Multiple Model
33%
Computer Science
Predictor Variable
100%
Machine Learning
50%
Predictive Performance
50%
Visual Display
50%
Comparative Analysis
50%
Prediction Error
50%
Data Mining Model
50%
Engineering
Black Box
100%
End-Users
66%
Predictor Variable
66%
Comparative Analysis
33%
Prediction Error
33%
Expected Risk
33%
Chemical Engineering
Learning System
100%