IBM Data Science Test 2025 – 400 Free Practice Questions to Pass the Exam

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In classification problems, what does the term "minority class" refer to?

The class with the most instances

The class that is not being predicted

The class that has fewer instances compared to the other classes

In classification problems, the term "minority class" specifically refers to the class that has fewer instances compared to other classes in the dataset. This is a critical concept in the context of classification tasks, especially when dealing with imbalanced datasets, where one class significantly outnumbers the other.

When training machine learning models, a minority class may not receive sufficient representation during training, leading to performance issues where the model may become biased towards the majority class. This imbalance can result in poor prediction accuracy for the minority class, as the model does not learn to recognize its features as effectively.

Understanding the definition of the minority class is crucial for implementing strategies such as oversampling, undersampling, or applying specialized algorithms that can help address the challenges posed by class imbalances, thereby improving the model's performance on both classes.

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The class that has equal instances as others

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