Which of the following terms best describes the diversity of outcomes that a model can produce due to variability in the data?

The term "variance" is the most appropriate choice for describing the diversity of outcomes a model can produce due to variability in the data. Variance in this context refers to how much the predictions of a model fluctuate when different datasets are used for training. A model with high variance pays too much attention to the training data, capturing noise along with the underlying patterns, which leads to excessive fluctuation in the outcomes based on minor changes in the input data.

In the realm of data science, this concept is critical for understanding model performance. A model that demonstrates high variance might perform exceptionally well on the training data but tends to perform poorly on unseen data, showing that it has not generalized well. This over-sensitivity to the particularities of the training set can create diverse and inconsistent predictions when new, slightly different data is fed into the model.

The other terms—selection bias, overfitting, and standardization—focus on different aspects of data modeling. Selection bias refers to errors caused by selecting non-representative samples from a population, which can skew results. Overfitting relates to a model that is too complex, capturing noise instead of the true underlying patterns, and is closely tied to high variance but is not the term that encapsulates the