The correct answer is: B. Filling in missing values in a dataset.
Imputation is the process of filling in missing values in a dataset. This can be done using a variety of methods, such as mean imputation, median imputation, or multiple imputation.
Mean imputation is the simplest method of imputation. It involves replacing each missing value with the mean of the non-missing values for that variable.
Median imputation is similar to mean imputation, but it replaces each missing value with the median of the non-missing values for that variable.
Multiple imputation is a more sophisticated method of imputation. It involves creating multiple datasets, each with a different set of missing values. The missing values are then filled in using one of the aforementioned methods. The results from the multiple datasets are then combined to produce a final dataset.
Imputation is an important step in data preprocessing. It can help to improve the accuracy of your results and make your data more useful for analysis.
A. Creating new features is the process of creating new variables from existing variables. This can be done by combining existing variables, transforming existing variables, or creating new variables from scratch.
C. Encoding categorical data is the process of converting categorical data into numerical data. This can be done using a variety of methods, such as one-hot encoding or ordinal encoding.
D. Creating new features is not a step in data preprocessing.