The most likely concern with a one-feature, machine-learning model is high error due to:

→ A one-feature model is likely to be overly simplistic and may not capture the true complexity of the target variable. This leads to underfitting, which is associated with high bias — the model consistently misses the mark regardless of the data.

Why the other options are incorrect:

B: High dimensionality is not a concern in this case — the model has too few features.

C: Variance refers to overfitting — more common in overly complex models.

D: Probability is a modeling technique, not a source of error.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 4.2:“Models with insufficient features tend to underfit and exhibit high bias due to their inability to represent complex relationships.”

Bias-Variance Tradeoff – Data Science Textbook:“A high-bias model makes strong assumptions and is typically too simple to capture the underlying patterns in data.”