Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 15 discussion

A. Yes, concept drift is an evolution of data that invalidates the data model. It happens when the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. This causes problems because the predictions become less accurate as time passes. B. No, if it was the case the F1 would have been low since the begin and this is not justifying a change after months C. No, same as B D. No, incorrect labels in the baseline calculations would undermine F1 baseline value, but this is not explain a significant drop after months

Concept Drift: Occurs when the statistical properties of the data used for predictions change over time, causing the model to underperform on current data. Why Not the Other Options? B. If the model complexity was insufficient, the issue would have been detected during the initial evaluation or baseline analysis, not after months of stable performance. C. A data quality issue would have impacted the model's performance immediately after deployment, not months later. D. Incorrect labels during baseline calculation could result in an inaccurate baseline F1 score, but it wouldn't explain a significant drop after stable performance over months.

Concept Drift: Refers to the change in the statistical properties of the underlying data distribution over time --> Decrease F1 score --> perform poorly on new data

Option A could be the only one possible reason for drifting "after several months".