•1 min read•from Data Science
Rescaling logistic regression predictions for under-sampled data?
Our take
When building a predictive model for a heavily imbalanced dataset, particularly with a binary outcome, it's crucial to address how under-sampling impacts your logistic regression predictions. Under-sampling the majority class might lead to skewed probability predictions, necessitating careful consideration of the optimal threshold for classification. Rescaling these predictions can enhance your model's performance by ensuring that the chosen threshold reflects the underlying distribution of your data. This approach empowers you to make informed decisions, improving the accuracy and reliability of your predictions.
I'm building a predictive model for a large dataset with a binary 0/1 outcome that is heavily imbalanced.
I'm under-sampling records from the majority outcome class (the 0s) in order to fit the data into my computer's memory prior to fitting a logistic regression model.
Because of the under-sampling, do I need to rescale the model's probability predictions when choosing the optimal threshold or is the scale arbitrary?
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