You have a dataset in which the 'income' feature has some missing values. You decided to use mode imputation. Why could this lead to misleading results?
- All of the above
- Income is usually a continuous variable, and mode may not be an appropriate measure of central tendency
- It could cause overfitting
- It might introduce selection bias
If the 'income' feature, typically a continuous variable, has some missing values and mode imputation is used, it could lead to misleading results. The mode is a measure of central tendency more suitable for categorical variables, not for continuous ones like income, and hence might not accurately reflect the underlying data distribution.
Loading...
Related Quiz
- Suppose the Variance Inflation Factor (VIF) of a variable in your model is 10. What does this imply and what actions would you take?
- What does the term "Multicollinearity" refer to in the context of Exploratory Data Analysis?
- Standardization or z-score normalization is a scaling technique where the values are centered around the _____ with a unit _____.
- Your organization has collected a large dataset from their latest marketing campaign and they want you to generate actionable insights from this data. Which type of data analysis would be the most suitable for this situation?
- Imagine you are working with a data set that includes survey responses on a 1-5 scale (1=Very Unsatisfied, 5=Very Satisfied). How would you classify this data type?