Databricks Machine Learning Professional Question 247
Select 3You are monitoring a categorical feature used in a machine learning model and notice that the distribution of its values has shifted significantly compared to the training data. Which of the following methods can help identify and address categorical feature drift effectively?
- A
Calculate the mode of the categorical feature in the new data and compare it to the mode in the training data.
- B
Count the unique values in the new data and compare them to the unique values in the training data.
- C
Replace missing values in the categorical feature with a constant value to prevent feature drift.
- D
Track the frequency distribution of each category over time to detect changes.
- E
Use one-hot encoding to prevent feature drift in categorical features.
Show answer and explanation
Correct answers: A, B, D
Explanation
Categorical feature drift occurs when the distribution of a categorical variable changes between the training and new data. Simple methods like comparing the mode, counting unique values, and monitoring frequency distributions are effective for detecting and addressing drift. Handling missing values or applying encoding techniques, while useful for data preprocessing, do not directly address drift.
- A. Correct.
Correct: Comparing the mode of the categorical feature between the new data and the training data is a simple and effective way to detect drift, as significant changes in the most frequent value may indicate drift.
- B. Correct.
Correct: Counting unique values and comparing them can identify situations where new or missing categories appear in the data, signaling potential drift.
- C. Incorrect.
Incorrect: While replacing missing values can improve data quality, it does not directly address feature drift. Feature drift involves changes in the distribution, not just handling missing data.
- D. Correct.
Correct: Tracking the frequency distribution of each category over time helps detect subtle changes in categorical feature distributions, which is a key step in identifying drift.
- E. Incorrect.
Incorrect: One-hot encoding is a feature engineering technique used to transform categorical variables for use in machine learning models. It does not inherently prevent or address feature drift.