Data Preprocessing Questions
Data imputation using random forests is a technique used in data preprocessing to fill in missing values in a dataset. It involves using a random forest algorithm to predict the missing values based on the other variables in the dataset. The random forest model is trained on the available data with complete information and then used to predict the missing values. This approach takes into account the relationships and patterns present in the data to make accurate imputations.