rgf-sklearn's Introduction

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

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