The correct answer is D. all of the mentioned.
Data imputation is the process of filling in missing values in a data set. It can be done using a variety of methods, including:
- Mean imputation: This method replaces missing values with the mean of the observed values.
- Median imputation: This method replaces missing values with the median of the observed values.
- Mode imputation: This method replaces missing values with the mode of the observed values.
- KNN imputation: This method replaces missing values with the values of the k nearest neighbors.
- Bayesian imputation: This method uses a Bayesian model to predict the missing values.
Data imputation can be done before or after data preprocessing. Preprocessing is the process of cleaning and transforming data. It can be done to remove outliers, reduce dimensionality, and normalize data.
Data imputation can also be done after data preprocessing. This is often done when the preprocessing steps have removed some of the information that is needed to impute the missing values.
The best method for data imputation depends on the type of data, the amount of missing data, and the desired level of accuracy.
In the case of the question, all of the mentioned methods can be used to impute data sets based only on information in the training set.