Databricks Machine Learning Associate Question 514
Single answerYou are working on a machine learning project in Databricks and want to compare the relationship between two continuous features: 'age' and 'income'. Which of the following methods is the most appropriate to assess the correlation between these two features?
- A
Chi-squared test
- B
Pearson correlation coefficient
- C
ANOVA (Analysis of Variance)
- D
Spearman correlation coefficient
Show answer and explanation
Correct answer: B
Explanation
To compare two continuous features, the Pearson correlation coefficient is the most commonly used method if the relationship is expected to be linear. It quantifies the strength and direction of the linear association between two variables. While other methods like Spearman correlation exist, they are typically used for non-linear or monotonic relationships. In this case, the problem does not indicate the need for such methods, so Pearson correlation is the most appropriate choice.
- A. Incorrect.
The Chi-squared test is used to determine the association between two categorical variables, not continuous variables. It is not appropriate for comparing 'age' and 'income'.
- B. Correct.
The Pearson correlation coefficient is a statistical measure used to assess the linear relationship between two continuous variables. It is the most appropriate method for comparing 'age' and 'income' in this scenario.
- C. Incorrect.
ANOVA is used to compare means across multiple groups or categories. It is not suitable for evaluating relationships between two continuous variables like 'age' and 'income'.
- D. Incorrect.
The Spearman correlation coefficient measures the monotonic relationship between two variables, which can be useful if the relationship is not linear. However, since no such assumption is stated in the question, Pearson correlation is a better choice for linear relationships.