Databricks Machine Learning Associate Question 513
Single answerYou are working on a machine learning project in Databricks and want to analyze the relationship between two continuous features, 'age' and 'salary'. Which of the following methods is most appropriate for this task?
- A
Pearson correlation coefficient
- B
Chi-square test
- C
ANOVA test
- D
Box plot visualization
Show answer and explanation
Correct answer: A
Explanation
To analyze the relationship between two continuous features, the Pearson correlation coefficient is the most appropriate method. It quantifies the linear relationship between the features, while other methods like Chi-square, ANOVA, or box plots serve different purposes and are not suitable for this scenario.
- A. Correct.
The Pearson correlation coefficient is a statistical method used to measure the strength and direction of the linear relationship between two continuous variables, making it the correct choice for analyzing 'age' and 'salary'.
- B. Incorrect.
The Chi-square test is used to assess the relationship between two categorical variables, so it is not appropriate for continuous features like 'age' and 'salary'.
- C. Incorrect.
The ANOVA test is used to compare the means of three or more groups, which is not relevant for analyzing the relationship between two continuous features.
- D. Incorrect.
A box plot is a visualization tool that summarizes the distribution of a variable, but it does not measure the relationship or correlation between two continuous features.