Databricks Machine Learning Professional Question 249
Select 3A team of data scientists is monitoring a production machine learning model and is concerned about feature drift in a numeric input feature, 'Age'. They initially used summary statistics such as mean and standard deviation to monitor this feature but found the method insufficient to capture subtle changes in the data distribution. Which of the following reasons explain why hypothesis tests or statistical tests are a more robust solution for monitoring numeric feature drift?
- A
Statistical tests can detect changes in the entire distribution of a feature, not just specific summary statistics like the mean or variance.
- B
Summary statistics are sensitive to outliers, which can lead to false conclusions about feature drift.
- C
Statistical tests provide a quantifiable p-value that indicates the likelihood of drift, offering a more interpretable metric for monitoring.
- D
Summary statistics can capture complex, nonlinear changes in the distribution of a numeric feature.
- E
Statistical tests require a significantly larger sample size than summary statistics to detect drift.
Show answer and explanation
Correct answers: A, B, C
Explanation
Statistical tests are a stronger monitoring solution for numeric feature drift because they evaluate the entire feature distribution, are less sensitive to outliers, and provide interpretable metrics like p-values for assessing drift significance. In contrast, summary statistics focus on specific aspects of the data (e.g., mean, variance) and may fail to detect subtle or nonlinear distribution shifts, limiting their robustness as a monitoring tool.
- A. Correct.
Statistical tests like KS-test or Chi-square test can evaluate the entire distribution of a feature, making them more robust for capturing subtle or complex distribution changes compared to simple summary statistics.
- B. Correct.
Summary statistics such as mean and variance can be skewed by outliers, potentially leading to incorrect conclusions about whether drift has occurred.
- C. Correct.
Statistical tests output a p-value, which provides a clear and interpretable metric for determining if observed changes are statistically significant, making them highly effective for monitoring.
- D. Incorrect.
Summary statistics are limited in their ability to capture changes beyond basic measures like mean or variance, and cannot detect complex or nonlinear shifts in a distribution.
- E. Incorrect.
While statistical tests may require a larger sample size for accurate detection, this is not a primary reason why they are more robust compared to summary statistics.