How do I calculate class weights for imbalanced datasets?
An example of an AI chatbot conversation about calculating class weights for imbalanced datasets.
How do I calculate class weights for imbalanced datasets?
To calculate class weights, you can use the formula: weight = total samples / (number of classes * samples in class). Would you like an example?
Yes, please provide an example with numbers.
Sure! If you have 100 samples in total, with 90 in class A and 10 in class B, the weights would be: Class A = 100 / (2 * 90) = 0.555, Class B = 100 / (2 * 10) = 5.0.
How can I implement this in Python?
You can use the compute_class_weight function from sklearn.utils.class_weight. It automatically calculates weights for you.
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