Exam Professional Machine Learning Engineer topic 1 question 76 discussion

You are working with time series data yet used random cross-validation, and you immediately achieved an extremely high AUC (99%) with little effort. This is a red flag for data leakage—meaning information from the future (or directly from the target) is leaking into the training process C is better answer

random cross-validation time series data -> B

B with nested cross validation.

"99% on training data" -> Data leakage "random cross-validation" -> Not suitable for time series, use "nested cross-validation"

Options B and C (Address data leakage by applying nested cross-validation during model training; Address data leakage by removing features highly correlated with the target value) are less relevant in this scenario because the primary concern appears to be overfitting rather than data leakage. Data leakage typically involves inadvertent inclusion of information from the test set in the training process, which may lead to overly optimistic performance metrics. However, there is no indication that data leakage is the cause of the high AUC ROC value in this case.

Options A and B also address overfitting, but they involve different strategies. Option A suggests using a less complex algorithm and k-fold cross-validation. While this can be effective, it might be premature to change the algorithm without first exploring hyperparameter tuning. Option B suggests addressing data leakage, which is a different issue and may not be the primary cause of overfitting in this scenario.

B with nested cross validation.

Went with B

Nested cross-validation to reduce data leakage - same as a previous question.

It`s B

B (same question 48) - https://towardsdatascience.com/time-series-nested-cross-validation-76adba623eb9

To say overfitting, I should have results on testing data, so it's data leakage. Common sense excludes C, so it's B.