Databricks Machine Learning Professional Question 57
Single answerYou are building a machine learning pipeline in Databricks to predict customer churn for a subscription service. The dataset contains a mix of numerical, categorical, and timestamp fields. During preprocessing, you notice that one of the features, 'last_login_time', is a timestamp column but is not directly usable for machine learning. How should you preprocess this column to make it suitable for modeling?
- A
Convert the timestamp to a numerical representation, such as the number of days since a reference date.
- B
Drop the 'last_login_time' column entirely, as timestamps are not useful for machine learning.
- C
One-hot encode the timestamp to represent it as categorical data.
- D
Replace the timestamp with a string representation of the date and time.
Show answer and explanation
Correct answer: A
Explanation
Timestamp data needs to be transformed into a numerical format to be usable in machine learning models. Converting the 'last_login_time' column to a numerical representation, such as the number of days since a reference date, captures the time-related information in a way that the model can interpret effectively. Dropping the column or encoding it improperly would result in loss or misinterpretation of valuable data.
- A. Correct.
Converting the timestamp to a numerical representation, such as the number of days since a reference date, is a common and effective way to preprocess timestamp data. This allows the model to interpret the time-related information in a meaningful way.
- B. Incorrect.
Dropping the timestamp column is not recommended unless the feature is proven to be irrelevant. Timestamp data often provides valuable information for predictive modeling, such as trends or recency.
- C. Incorrect.
One-hot encoding is not suitable for timestamp data because timestamps are continuous and not inherently categorical. Representing them as categorical data could lead to a large number of unnecessary features, harming model performance.
- D. Incorrect.
Replacing the timestamp with a string representation is not valid for machine learning, as models cannot interpret string representations of dates and times meaningfully.