Scikit-learn API toy wrapper for Regularized Greedy Forests
Python
Latest commit c2e4bff Apr 29, 2016 MLWave mark-up

readme.md

Regularized Greedy Forest Wrappers

First version for a toy Scikit/learn API compatible wrapper for Regularized Greedy Forests [Johnson & Zhang, 2014]

Usage

Classification

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

Regression

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