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Q16 Single choice

A data scientist is tasked with building a predictive model for a healthcare application that identifies patients at risk of developing a rare disease. The dataset is highly imbalanced, with only 1% of the samples representing patients with the disease. This imbalance is causing the model to predict the majority class (healthy patients) almost exclusively, resulting in poor detection of the rare disease cases. They need to address this class imbalance to improve the model's performance on the minority class.

Which strategy should they implement to effectively mitigate the class imbalance in their training data?

A
Apply an oversampling technique to increase the number of samples from the minority class (patients with the disease).
B
Perform dimensionality reduction to decrease the number of features in the dataset
C
Increase the number of epochs during model training to allow the model to learn better from the data.
D
Implement a cross-validation technique to ensure the model generalizes well to unseen data.

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