Missing values in supervised learning can be a major issue, as they can reduce the accuracy of the model and lead to poor performance. Several approaches can be taken to address missing values in supervised learning, including:

1. Deletion: One approach to dealing with missing values is simply deleting any instances or features containing missing values. This can be done by removing rows containing missing values or columns containing missing values. While this approach is simple and easy to implement, it can result in the loss of a significant amount of data, which can negatively impact the accuracy of the model.
2. Imputation: Another approach to dealing with missing values is to impute them or fill them in with some estimated value. This can be done by using statistical measures such as mean, median, or mode to fill in the missing values. Alternatively, more advanced techniques can be used, such as multiple imputation or hot-deck imputation. While imputation can help retain more original data, it can also introduce bias if the imputed values do not represent the true values.
3. Prediction: A third approach to dealing with missing values is to use a separate model to predict the missing values. This approach can be effective if the missing values are not randomly distributed and can be accurately predicted using other features in the dataset. However, this approach can be time-consuming and may not always be practical.
4. Use of algorithms that can handle missing values: Some algorithms, such as decision trees and random forests, can handle missing values and do not require them to be imputed or deleted. These algorithms can be used as an alternative to traditional imputation methods, particularly if the missing values are not easily imputable using statistical measures.

Ultimately, the best approach to dealing with missing values in supervised learning will depend on the dataset’s specific characteristics and the modeling exercise’s goals. It may be necessary to try out multiple approaches and compare their performance to find the most effective solution.