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 that can be used to address the issue of imbalanced data in a dataset that has continuous features. It provides multiple-choice options related to managing class distribution in data.
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 using techniques like SMOTE can help balance a dataset by creating new, artificial samples for the minority class. This maintains the variance and distribution of features in continuous data.
Tips
One common mistake is to over-sample the minority class without ensuring that the new synthetic samples are diverse enough, which can lead to overfitting.
Sources
- 8 Tactics to Combat Imbalanced Classes in Your Machine Learning - machinelearningmastery.com
- Handling Imbalanced Datasets in Python: Methods and Procedures - medium.com
- SMOTE: Synthetic Minority Over-sampling Technique - arxiv.org
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