What technique can be employed to handle imbalanced data in a dataset with continuous features? a) over-sampling the majority class b) under-sampling the minority class c) generati... What technique can be employed to handle imbalanced data in a dataset with continuous features? a) over-sampling the majority class b) under-sampling the minority class c) generating synthetic samples for the minority class d) removing features with low variance
Understand the Problem
The question is asking about techniques to manage imbalanced datasets that contain continuous features, specifically among options related to sampling methods and feature selection.
Answer
Generating synthetic samples for the minority class.
The final answer is generating synthetic samples for the minority class.
Answer for screen readers
The final answer is generating synthetic samples for the minority class.
More Information
Generating synthetic samples, such as using the SMOTE technique, helps to balance the distribution of classes by creating new artificial samples for the minority class without duplicating existing samples.
Tips
A common mistake is to confuse over-sampling (copying existing samples) with generating synthetic samples, which creates new and potentially more informative data points.
Sources
- 10 Techniques to handle imbalance class in Machine Learning - analyticsvidhya.com
- 8 Tactics to Combat Imbalanced Classes in Your Machine Learning - machinelearningmastery.com
- 5 Techniques to Handle Imbalanced Data For a Classification Problem - analyticsvidhya.com
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