What are the risks of oversampling in data analysis?
An example of an AI chatbot conversation about the risks of oversampling in data analysis.
What are the risks of oversampling in data analysis?
Oversampling can lead to overfitting, where the model learns noise instead of the underlying pattern.
Can you explain how overfitting occurs due to oversampling?
Sure! Overfitting happens when a model is trained too well on the training data, including its noise, making it perform poorly on unseen data.
Are there any specific techniques to mitigate the risks of oversampling?
Yes, techniques like SMOTE (Synthetic Minority Over-sampling Technique) and careful validation can help reduce these risks.
What are the alternatives to oversampling?
Alternatives include undersampling the majority class, using ensemble methods, or generating synthetic data.
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