Why is training a random forest regressor with MAE criterion so slow compared to MSE?

Question

When training on even small applications (<50K rows <50 columns) using the mean absolute error criterion for sklearn\'s RandomForestRegress is nearly 10x slower than u

Answer 1

Thank you @hellpanderr for sharing a reference to the project issue. To summarize – when the random forest regressor optimizes for MSE it optimizes for the L2-norm and a mean-based impurity metric. But when the regressor uses the MAE criterion it optimizes for the L1-norm which amounts to calculating the median. Unfortunately, sklearn's the regressor's implementation for MAE appears to take O(N^2) currently.