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Can logistic regression handle imbalanced data?
Logistic regression does not support imbalanced classification directly. Instead, the training algorithm used to fit the logistic regression model must be modified to take the skewed distribution into account. How logistic regression can be modified to weight model error by class weight when fitting the coefficients.
Does an unbalanced sample matter when doing logistic regression?
3 Answers. For logistic regression models unbalanced training data affects only the estimate of the model intercept (although this of course skews all the predicted probabilities, which in turn compromises your predictions).
Why does an unbalanced sample matter when doing logistic regression?
For logistic regression models unbalanced training data affects only the estimate of the model intercept (although this of course skews all the predicted probabilities, which in turn compromises your predictions).
How to calculate weighted logistic regression for imbalanced dataset?
Weights associated with classes in the form {class_label: weight}. If not given, all classes are supposed to have same weight value. With weighted-LG, Area-Under-Curve (AUC) increased drastically from 0.7412 to 0.8802.
Why are minority classes imbalanced in logistic regression?
The problem is not that the classes are imbalanced per se, it is that there may not be sufficient patterns belonging to the minority class to adequately represent its distribution. This means that the problem can arise for any classifier (even if you have a synthetic problem and you know you have the true model), not just logistic regression.
Why is imbalanced distribution a challenge for class prediction?
Such high imbalanced distribution pose a challenge for class prediction. The reason being most of the classifiers are designed or have default values assuming equal distribution of each label. Slight imbalance does not pose any challenge and can be treated like a normal classification problem.