Databricks Machine Learning Professional Question 246
Select 3You are monitoring a deployed machine learning model and notice a significant drift in a key categorical feature. To address this, you decide to analyze the mode, unique values, and missing values of the feature. What insights can these analyses provide to help mitigate categorical feature drift?
- A
The mode can help identify changes in the most frequently occurring category, which may indicate a shift in the data distribution.
- B
Analyzing unique values can reveal the introduction of previously unseen categories, which might cause model performance issues.
- C
Identifying missing values can indicate data quality issues or changes in data collection processes that might be causing the drift.
- D
Using the mode to replace all missing values in the feature will always eliminate the drift completely.
- E
Unique values analysis can help predict the target variable directly without further model adjustments.
Show answer and explanation
Correct answers: A, B, C
Explanation
Categorical feature drift can arise from changes in the distribution, the introduction of new categories, or issues like missing values. Analyzing the mode, unique values, and missing values provides insights into the nature and cause of the drift. These analyses help inform strategies to mitigate the drift, such as retraining the model or updating preprocessing steps. However, simplistic actions like replacing missing values with the mode or assuming unique value analysis can directly predict outcomes are not sufficient solutions.
- A. Correct.
The mode is the most frequent value in the feature. A change in the mode could indicate that the distribution of the categorical data has shifted, which is an important sign of feature drift.
- B. Correct.
Analyzing unique values helps detect the presence of new categories not seen during model training. These new values can lead to errors if the model cannot handle them properly.
- C. Correct.
Missing values might signify changes in data collection processes or data quality issues that could cause categorical feature drift. Identifying and addressing these issues is essential.
- D. Incorrect.
Replacing missing values with the mode might reduce some effects of drift but does not necessarily eliminate it completely, as drift can occur due to other causes such as changes in distributions or new categories.
- E. Incorrect.
Unique values analysis alone cannot predict the target variable. It is a diagnostic tool to identify drift or data issues, not a solution for prediction.