Java machine learning library.
final int trees = 30; // number of trees to learn final int maxDepth = 6; // maximum tree depth final int minObs = 10; // min. observation in tree nodes final double isample = 0.9; // sample fraction of data points per tree final double fsample = 0.8; // sample fraction of features per tree final VoteStrategy strategy = VoteStrategy.AVERAGE; final RandomForest learner = new RandomForest(trees, maxDepth, minObs, isample, fsample, strategy); // create your list of training data points here final Collection<SimpleSample> trainingData = ...; final IModel<ISample, Double> model = learner.run(trainingData); // create your list of testing data points here final Collection<ISample> test = ...; final Double predictions = model.apply(new Samples<>(test));
Copyright (C) 2016 Stefan Henß Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.