Let’s say, you are working with categorical feature(s) and you have not looked at the distribution of the categorical variable in the test data. You want to apply one hot encoding (OHE) on the categorical feature(s). What challenges you may face if you have applied OHE on a categorical variable of train dataset?

The correct answer is: Both A and B.

One hot encoding (OHE) is a technique used to convert categorical features into numerical features. It does this by creating a new feature for each unique category in the original feature. For example, if a feature has the categories “red”, “blue”, and “green”, OHE would create three new features, one for each category.

One challenge that can arise when using OHE is that the distribution of categories in the test data may be different from the distribution of categories in the training data. This can happen if the test data is collected from a different population than the training data. If the distribution of categories is different, then the OHE features may not be as effective in predicting the target variable in the test data.

Another challenge that can arise when using OHE is that some categories may not be present in the test data. This can happen if the test data is collected from a different population than the training data, or if the test data is collected from a different time period than the training data. If some categories are not present in the test data, then the OHE features will not be able to capture the information about those categories.

To avoid these challenges, it is important to check the distribution of categories in the test data before using OHE. If the distribution of categories is different from the distribution of categories in the training data, then you may need to adjust the OHE features or collect more training data. If some categories are not present in the test data, then you may need to remove those categories from the training data or create new OHE features for them.