About: In this paper, we investigate potential biases in datasets used to make drug binding predictions using machine learning. We investigate a recently published metric called the Asymmetric Validation Embedding (AVE) bias which is used to quantify this bias and detect overfitting. We compare it to a slightly revised version and introduce a new weighted metric. We find that the new metrics allow to quantify overfitting while not overly limiting training data and produce models with greater predictive value.   Goto Sponge  NotDistinct  Permalink

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  • In this paper, we investigate potential biases in datasets used to make drug binding predictions using machine learning. We investigate a recently published metric called the Asymmetric Validation Embedding (AVE) bias which is used to quantify this bias and detect overfitting. We compare it to a slightly revised version and introduce a new weighted metric. We find that the new metrics allow to quantify overfitting while not overly limiting training data and produce models with greater predictive value.
subject
  • Learning
  • Statistical inference
  • Regression analysis
  • Machine learning
  • Cybernetics
  • Validity (statistics)
  • AVE high-speed trains
  • High-speed rail in Spain
  • Renfe Operadora
  • Datasets in machine learning
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