First version for a toy Scikit/learn API compatible wrapper for Regularized Greedy Forests [Johnson & Zhang, 2014]
RegularizedGreedyForestClassifier(verbose=0, max_leaf=500, test_interval=100, loc_exec=loc_exec, loc_temp=loc_temp, algorithm="RGF", loss="LS", l2="1", prefix="model")
| Parameter | Description |
|---|---|
| verbose | Int. Verbosity of the classifier. Default=0 |
| max_leaf | Int. Max number of leafs to create before halting. Default=500 |
| test_interval | Int. Save models during intervals. Default=100 |
| algorithm | String. Any of RGF (RGF with L2 regularization, RGF_Opt (RGF with min-penalty regularization), RGF_Sib (RGF with min-penalty regularization with sum-to-zero sibling constraints) Default=RGF |
| loss | String. Any of LS (Least squares), Expo (Exponential), Log (Logarithmic). Default=LS |
| L2 | Float. Amount of L2 regularization. 1.0, 0.1 and 0.01 are sane values. Default=1.0 |
RegularizedGreedyForestRegressor(verbose=0, max_leaf=500, test_interval=100, loc_exec=loc_exec, loc_temp=loc_temp, algorithm="RGF", loss="LS", l2="1", prefix="model")
| Parameter | Description |
|---|---|
| verbose | Int. Verbosity of the regressor. Default=0 |
| max_leaf | Int. Max number of leafs to create before halting. Default=500 |
| test_interval | Int. Save models during intervals. Default=100 |
| algorithm | String. Any of RGF (RGF with L2 regularization, RGF_Opt (RGF with min-penalty regularization), RGF_Sib (RGF with min-penalty regularization with sum-to-zero sibling constraints) Default=RGF |
| loss | String. Any of LS (Least squares), Expo (Exponential), Log (Logarithmic). Default=LS |
| L2 | Float. Amount of L2 regularization. 1.0, 0.1 and 0.01 are sane values. Default=1.0 |