Explanation:
The best approach is to introduce a new category for missing values within Feature A and create a binary indicator feature. This method preserves the predictive power of Feature A by maintaining its categorical nature and explicitly accounts for the missingness, allowing the model to learn from this pattern. Imputing missing values with the mode or a correlated feature can introduce bias by assuming missing values are similar to observed ones. Dropping Feature A could lead to significant information loss, especially given its predictive importance. The proposed solution effectively handles missing values without introducing bias or losing valuable information.
Ultimate access to all questions.
No comments yet.
You are working on a machine learning project where you have identified a categorical feature, Feature A, during exploratory data analysis. Feature A shows significant predictive power for your target variable but is found to have missing values in approximately 10% of the dataset. The dataset is large, and the missingness in Feature A is believed to be random. Given the importance of Feature A and the need to maintain the integrity of your model's predictions, which of the following approaches is the BEST course of action? Choose one correct option.
A
Impute the missing values in Feature A with the mode of the feature, assuming that the most common category is the best replacement for missing data.
B
Replace the missing values in Feature A with values from the feature that has the highest Pearson correlation with Feature A, under the assumption that correlated features can provide reasonable substitutes.
C
Remove Feature A from the dataset if more than 15% of its values are missing, to avoid the complexity of handling missing data, despite its predictive power.
D
Introduce a new category within Feature A to represent missing values and create an additional binary feature indicating the presence or absence of a value in Feature A, to explicitly model the missingness.