One-hot encoding involves creating a new binary column for each unique value in the 'Color' feature, excluding the mode value.

After imputing missing values with the mode, one-hot encoding will create three new binary columns for 'Red', 'Green', and 'Blue', with one column having a value of 1 for each instance and the rest as 0s.

Tree-based models can efficiently handle one-hot encoded features without any issues, as they can split on these binary features directly.

One-hot encoding the 'Color' feature after imputing with the mode value will create three new binary columns, but this approach can be inefficient for tree-based models as it may lead to imbalanced splits and overfitting.