IBM Data Science Practice Test

What does a Gaussian mixture model primarily consist of?

1. Multiple linear regression models combined.

2. A mixture of univariate Gaussian distributions.

3. Multiple non-linear decision trees.

4. Single multivariate Gaussian distribution.

The correct answer is: A mixture of univariate Gaussian distributions.

A Gaussian mixture model is fundamentally comprised of a combination of multiple Gaussian distributions, which can be either univariate or multivariate. The primary feature of this model is its ability to represent complex data distributions as a weighted sum of these Gaussian components. Each individual Gaussian in the mixture is characterized by its own mean and variance, allowing the model to capture different subpopulations within the overall data. In the context of option B, the phrase "mixture of univariate Gaussian distributions" specifically highlights that the model can use multiple Gaussian distributions to model data that can be described along one dimension. While Gaussian mixture models can also extend to multivariate settings, the essence remains that they are composed of various Gaussian distributions, each indicating different clusters or groups within the data. This flexibility makes Gaussian mixture models particularly useful for clustering tasks in unsupervised learning contexts, where the goal is to discover natural groupings in the data. In contrast, other options describe different concepts that do not pertain to the structure of Gaussian mixture models. For instance, combining multiple linear regression models does not capture the underlying distributional characteristics of the data in the same way as Gaussian mixtures. Decision trees involve a completely different mathematical approach, and a single multivariate Gaussian distribution lacks the mixture aspect that