What is oversampling?
An example of an AI chatbot conversation about oversampling in data processing.
What is oversampling?
Oversampling is a technique used in data processing to increase the number of instances in a dataset, often to balance class distributions.
Why is oversampling important in machine learning?
Oversampling helps to prevent model bias by ensuring that minority classes are adequately represented, improving the model's performance.
What are some common methods of oversampling?
Common methods include Random Oversampling, SMOTE (Synthetic Minority Over-sampling Technique), and ADASYN.
Can oversampling lead to overfitting?
Yes, oversampling can lead to overfitting, especially if the same instances are duplicated, making the model too tailored to the training data.
How do I decide if I should use oversampling?
Consider using oversampling if you have an imbalanced dataset and your model is struggling to learn from the minority class.
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