【预测模型】基于极端随机树实现数据回归预测和分类附matlab代码

function [ensemble,output,scores,depths] = buildAnEnsemble(M,K,nmin,data,problemType,inputType,sampleWeights)%% Builds an ensemble of Extra-Trees for regression or classification% datasets%  % Inputs : % M             = number of trees in the ensemble% K             = number of attributes randomly selected at each node% nmin          = minimum sample size for splitting a node% data          = calibration dataset (targets are in the last column) % problemType   = specify problem type (1 for regression, zero for classification)% inputType     = binary vector indicating feature type(0:categorical,1:numerical)% sampleWeights = weights of the samples (used for IterativeInputSelection)% only include input type for classification problems% %% Outputs : % ensemble  = the ensemble, which is a M-long array of Extra-Tree structs  %             (see buildAnExtraTree for the details regarding each field)   % output    = predictions of the ensemble on the training data set %%%% Copyright 2015 Ahmad Alsahaf% Research fellow, Politecnico di Milano% ahmadalsahaf@gmail.com%% Copyright 2014 Riccardo Taormina % Ph.D. Student, Hong Kong Polytechnic University  % riccardo.taormina@gmail.com %% Please refer to README.txt for bibliographical references on Extra-Trees!%% This file is part of MATLAB_ExtraTrees%%     MATLAB_ExtraTrees is free software: you can redistribute it and/or modify%     it under the terms of the GNU General Public License as published by%     the Free Software Foundation, either version 3 of the License, or%     (at your option) any later version.% %     MATLAB_ExtraTrees is distributed in the hope that it will be useful,%     but WITHOUT ANY WARRANTY; without even the implied warranty of%     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the%     GNU General Public License for more details.% %     You should have received a copy of the GNU General Public License%     along with MATLAB_ExtraTrees_classification.  If not, see <http://www.gnu.org/licenses/>.if problemType == 0    [ensemble,output,scores,depths] = buildAnEnsemble_r(M,K,nmin,data);    else    [ensemble,output,scores,depths] = buildAnEnsemble_c(M,K,nmin,data,inputType,sampleWeights);%     [ensemble,output,scores,depths] = buildAnEnsemble_c(M,K,nmin,data,sampleWeights);end