One-hot encoding involves creating a new binary column for each unique value in the 'Marital Status' feature, resulting in four columns. This approach is inefficient for tree-based models because they cannot handle categorical features directly.

One-hot encoding involves creating a new binary column for each unique value in the 'Marital Status' feature, excluding one value to avoid multicollinearity. This approach is inefficient for tree-based models because it can lead to a large number of binary columns, increasing the dimensionality of the dataset.

One-hot encoding involves creating a new binary column for each unique value in the 'Marital Status' feature, resulting in three columns. This approach is inefficient for tree-based models because they can handle categorical features directly without the need for one-hot encoding.

One-hot encoding involves creating a new ordinal column for each unique value in the 'Marital Status' feature, assigning numerical values based on the order of the categories. This approach is inefficient for tree-based models because it introduces an arbitrary order to the categories.