What is class imbalance in machine learning?
An example of an AI chatbot conversation about class imbalance and importance sampling in ML.
What is class imbalance in machine learning?
Class imbalance occurs when the classes in a dataset are not represented equally, leading to biased models.
How does importance sampling help with class imbalance?
Importance sampling adjusts the training process by giving more weight to underrepresented classes, improving model performance.
Can you give an example of importance sampling in action?
Sure! In a dataset with 90% class A and 10% class B, importance sampling might sample class B more frequently to balance the training.
What are some techniques to handle class imbalance?
Techniques include resampling methods, using different algorithms, and applying cost-sensitive learning.
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