웹2014년 7월 22일 · I think it always depends on the scenario. Using a representative data set is not always the solution. Assume that your training set has 1000 negative examples and 20 positive examples. Without any modification of the classifier, your algorithm will tend to classify all new examples as negative. 웹2024년 6월 16일 · Now I have two options: Option 1) Step 1: Pull a randomly selected 200K imbalanced data for training (180K samples pos class vs 20K samples neg class) Step 2: During each CV iteration: The training fold will have 160K samples (144K pos vs 16K neg) and the validation fold will have 40K samples (36K pos vs 4K neg) Step 3: Apply data …
What Is Balanced And Imbalanced Dataset? by Himanshu Tripathi …
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Calibration, Imbalanced Data - GitHub Pages
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