You started working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?
A) Address the model overfitting by using a less complex algorithm.
B) Address data leakage by applying nested cross-validation during model training.
C) Address data leakage by removing features highly correlated with the target value.
D) Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.

Question

Quizplus · Accepted Answer

Data leakage occurs when information from the test set is unintentionally used to train the model, leading to an inflated AUC ROC value. Nested cross-validation helps prevent this by ensuring that the model is trained and evaluated on completely separate data sets.

You Started Working on a Classification Problem with Time Series