Imputing missing values using a decision tree involves predicting the absent values in a specific column based on other features in the dataset. Decision trees, a type of machine learning model, make decisions by following “if-then-else” rules based on input features, proving particularly adept at handling categorical data and intricate feature relationships. To apply this to a dataset, consider using a decision tree to impute missing values in the ‘armed’ column. Begin by ensuring other predictor columns are devoid of missing values and encoding categorical variables if necessary. Split the data into sets with known and missing ‘armed’ values, then train the decision tree using the former. Subsequently, use the trained model to predict and impute missing ‘armed’ values in the latter set. Optionally, evaluate the model’s performance using a validation set or cross-validation to gauge the accuracy of the imputation process.