Topic 1 Question 100
A Data Scientist is developing a binary classifier to predict whether a patient has a particular disease on a series of test results. The Data Scientist has data on 400 patients randomly selected from the population. The disease is seen in 3% of the population. Which cross-validation strategy should the Data Scientist adopt?
A k-fold cross-validation strategy with k=5
A stratified k-fold cross-validation strategy with k=5
A k-fold cross-validation strategy with k=5 and 3 repeats
An 80/20 stratified split between training and validation
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B - stratified k-fold cross-validation will enforce the class distribution in each split of the data to match the distribution in the complete training dataset.
👍 16scuzzy20102021/09/28B is the correct answer. Use Stratified k-Fold Cross-Validation for Imbalanced Classification. Stratified train/test splits is an option too. But the question is specifically asking "cross-validation" strategy.
👍 9SophieSu2021/10/05Why K=5?
👍 2AWS__Newbie2021/11/02
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