query: How to tune the proportion of minority class after oversampling in imbalanced data with cross-validation?
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positive_passages.text: Why you shouldn't upsample before cross validation
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negative_passages.text: Methods to work around the problem of missing data in machine learning
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negative_passages.text: Suggestions for cost-sensitive learning in a highly imbalanced setting
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negative_passages.text: Opinions about Oversampling in general, and the SMOTE algorithm in particular
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negative_passages.text: AUC and class imbalance in training/test dataset
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negative_passages.text: SVM for unbalanced data
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negative_passages.text: K-fold vs. Monte Carlo cross-validation
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negative_passages.text: Which metric to use to evaluate highly imbalance classification model performance
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negative_passages.text: Scikit Learn Missing Data - Categorical values
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negative_passages.text: How to judge if a supervised machine learning model is overfitting or not?
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negative_passages.text: Evaluating multiclass imbalanced problem per class
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negative_passages.text: What balancing method can I apply to an imbalanced data set?
negative_passages.docid: 31300
negative_passages.text: Does an unbalanced sample matter when doing logistic regression?
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negative_passages.text: How to set class weights for imbalanced classes in Keras?
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negative_passages.text: For imbalanced classification, should the validation dataset be balanced?
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negative_passages.text: How to Identify Overfitting in Convolutional Neural network?
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negative_passages.text: Finding Bias$(s^2)$ in incorrect linear model