A data scientist is tasked with building a predictive model for a rare disease, where only 1% of the patient data belongs to the positive class. To address this severe class imbalance, the data scientist decides to use the Synthetic Minority Oversampling Technique (SMOTE). Which of the following statements provides the most accurate description of the fundamental process SMOTE uses to generate new samples?
It duplicates existing minority class samples at random until the class distribution is balanced.
It removes samples from the majority class at random to create a more balanced class distribution.
It creates synthetic samples on the line segments that join an existing minority class instance with one of its k-nearest neighbors from the same minority class.
It generates new minority samples by creating a probability distribution from the features of the minority class and sampling from it.
The correct answer accurately describes the core mechanism of the Synthetic Minority Oversampling Technique (SMOTE). SMOTE works by first selecting an instance from the minority class at random. It then identifies that instance's 'k' nearest neighbors, which are also part of the minority class. A new, synthetic instance is created by interpolating between the selected instance and one of its randomly chosen neighbors. This process is repeated to generate a specified number of new minority class samples.
Incorrect options describe other data-balancing techniques or misconceptions about data generation:
Duplicating existing minority samples is known as Random Oversampling, a simpler technique that can lead to overfitting because it adds no new information to the model.
Removing samples from the majority class is a form of undersampling, such as Random Undersampling.
Generating samples from a learned probability distribution is a parametric approach to data generation, whereas SMOTE is a non-parametric method that operates locally in the feature space without assuming an underlying data distribution.
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How does SMOTE's synthetic sample creation prevent overfitting compared to Random Oversampling?