IBM Data Science Practice Test 2026 – Comprehensive Exam Prep

Naïve Bayes theorem fundamentally relies on a few key assumptions that align with the choices presented. It operates under the premise that the features used in classification tasks are conditionally independent given the class label. This characteristic is central to the method and is encapsulated in the idea that the presence of a particular feature does not influence the presence of another, which supports efficient computations.

Furthermore, prior probabilities indeed come into play in Naïve Bayes. These are derived from prior knowledge or frequency of occurrences, which can be interpreted as experience with the data that informs the model about the expected probabilities for each class before observing specific feature values.

Additionally, Naïve Bayes classifiers are particularly advantageous when handling high-dimensional data. This is due to their assumption of feature independence, which simplifies computations and allows for straightforward scalability in cases where the number of features is large.

Thus, all these attributes form a cohesive understanding of Naïve Bayes theorem, illustrating why the assertion that includes all these elements is indeed an accurate description of the theorem's characteristics.