This example builds a random forest with ALACART decision trees. Classification errors for the Iris data are shown using the out-of-bag predictions. An out-of-bag prediction for an observation is generated by models fitted to bootstrap samples that do not include the observation.
import com.imsl.math.*; import com.imsl.stat.*; import com.imsl.datamining.decisionTree.*; public class RandomTreesEx2 { public static void main(String[] args) throws Exception { double[][] irisFisherData = { {1.0, 5.1, 3.5, 1.4, .2}, {1.0, 4.9, 3.0, 1.4, .2}, {1.0, 4.7, 3.2, 1.3, .2}, {1.0, 4.6, 3.1, 1.5, .2}, {1.0, 5.0, 3.6, 1.4, .2}, {1.0, 5.4, 3.9, 1.7, .4}, {1.0, 4.6, 3.4, 1.4, .3}, {1.0, 5.0, 3.4, 1.5, .2}, {1.0, 4.4, 2.9, 1.4, .2}, {1.0, 4.9, 3.1, 1.5, .1}, {1.0, 5.4, 3.7, 1.5, .2}, {1.0, 4.8, 3.4, 1.6, .2}, {1.0, 4.8, 3.0, 1.4, .1}, {1.0, 4.3, 3.0, 1.1, .1}, {1.0, 5.8, 4.0, 1.2, .2}, {1.0, 5.7, 4.4, 1.5, .4}, {1.0, 5.4, 3.9, 1.3, .4}, {1.0, 5.1, 3.5, 1.4, .3}, {1.0, 5.7, 3.8, 1.7, .3}, {1.0, 5.1, 3.8, 1.5, .3}, {1.0, 5.4, 3.4, 1.7, .2}, {1.0, 5.1, 3.7, 1.5, .4}, {1.0, 4.6, 3.6, 1.0, .2}, {1.0, 5.1, 3.3, 1.7, .5}, {1.0, 4.8, 3.4, 1.9, .2}, {1.0, 5.0, 3.0, 1.6, .2}, {1.0, 5.0, 3.4, 1.6, .4}, {1.0, 5.2, 3.5, 1.5, .2}, {1.0, 5.2, 3.4, 1.4, .2}, {1.0, 4.7, 3.2, 1.6, .2}, {1.0, 4.8, 3.1, 1.6, .2}, {1.0, 5.4, 3.4, 1.5, .4}, {1.0, 5.2, 4.1, 1.5, .1}, {1.0, 5.5, 4.2, 1.4, .2}, {1.0, 4.9, 3.1, 1.5, .1}, {1.0, 5.0, 3.2, 1.2, .2}, {1.0, 5.5, 3.5, 1.3, .2}, {1.0, 4.9, 3.1, 1.5, .1}, {1.0, 4.4, 3.0, 1.3, .2}, {1.0, 5.1, 3.4, 1.5, .2}, {1.0, 5.0, 3.5, 1.3, .3}, {1.0, 4.5, 2.3, 1.3, .3}, {1.0, 4.4, 3.2, 1.3, .2}, {1.0, 5.0, 3.5, 1.6, .6}, {1.0, 5.1, 3.8, 1.9, .4}, {1.0, 4.8, 3.0, 1.4, .3}, {1.0, 5.1, 3.8, 1.6, .2}, {1.0, 4.6, 3.2, 1.4, .2}, {1.0, 5.3, 3.7, 1.5, .2}, {1.0, 5.0, 3.3, 1.4, .2}, {2.0, 7.0, 3.2, 4.7, 1.4}, {2.0, 6.4, 3.2, 4.5, 1.5}, {2.0, 6.9, 3.1, 4.9, 1.5}, {2.0, 5.5, 2.3, 4.0, 1.3}, {2.0, 6.5, 2.8, 4.6, 1.5}, {2.0, 5.7, 2.8, 4.5, 1.3}, {2.0, 6.3, 3.3, 4.7, 1.6}, {2.0, 4.9, 2.4, 3.3, 1.0}, {2.0, 6.6, 2.9, 4.6, 1.3}, {2.0, 5.2, 2.7, 3.9, 1.4}, {2.0, 5.0, 2.0, 3.5, 1.0}, {2.0, 5.9, 3.0, 4.2, 1.5}, {2.0, 6.0, 2.2, 4.0, 1.0}, {2.0, 6.1, 2.9, 4.7, 1.4}, {2.0, 5.6, 2.9, 3.6, 1.3}, {2.0, 6.7, 3.1, 4.4, 1.4}, {2.0, 5.6, 3.0, 4.5, 1.5}, {2.0, 5.8, 2.7, 4.1, 1.0}, {2.0, 6.2, 2.2, 4.5, 1.5}, {2.0, 5.6, 2.5, 3.9, 1.1}, {2.0, 5.9, 3.2, 4.8, 1.8}, {2.0, 6.1, 2.8, 4.0, 1.3}, {2.0, 6.3, 2.5, 4.9, 1.5}, {2.0, 6.1, 2.8, 4.7, 1.2}, {2.0, 6.4, 2.9, 4.3, 1.3}, {2.0, 6.6, 3.0, 4.4, 1.4}, {2.0, 6.8, 2.8, 4.8, 1.4}, {2.0, 6.7, 3.0, 5.0, 1.7}, {2.0, 6.0, 2.9, 4.5, 1.5}, {2.0, 5.7, 2.6, 3.5, 1.0}, {2.0, 5.5, 2.4, 3.8, 1.1}, {2.0, 5.5, 2.4, 3.7, 1.0}, {2.0, 5.8, 2.7, 3.9, 1.2}, {2.0, 6.0, 2.7, 5.1, 1.6}, {2.0, 5.4, 3.0, 4.5, 1.5}, {2.0, 6.0, 3.4, 4.5, 1.6}, {2.0, 6.7, 3.1, 4.7, 1.5}, {2.0, 6.3, 2.3, 4.4, 1.3}, {2.0, 5.6, 3.0, 4.1, 1.3}, {2.0, 5.5, 2.5, 4.0, 1.3}, {2.0, 5.5, 2.6, 4.4, 1.2}, {2.0, 6.1, 3.0, 4.6, 1.4}, {2.0, 5.8, 2.6, 4.0, 1.2}, {2.0, 5.0, 2.3, 3.3, 1.0}, {2.0, 5.6, 2.7, 4.2, 1.3}, {2.0, 5.7, 3.0, 4.2, 1.2}, {2.0, 5.7, 2.9, 4.2, 1.3}, {2.0, 6.2, 2.9, 4.3, 1.3}, {2.0, 5.1, 2.5, 3.0, 1.1}, {2.0, 5.7, 2.8, 4.1, 1.3}, {3.0, 6.3, 3.3, 6.0, 2.5}, {3.0, 5.8, 2.7, 5.1, 1.9}, {3.0, 7.1, 3.0, 5.9, 2.1}, {3.0, 6.3, 2.9, 5.6, 1.8}, {3.0, 6.5, 3.0, 5.8, 2.2}, {3.0, 7.6, 3.0, 6.6, 2.1}, {3.0, 4.9, 2.5, 4.5, 1.7}, {3.0, 7.3, 2.9, 6.3, 1.8}, {3.0, 6.7, 2.5, 5.8, 1.8}, {3.0, 7.2, 3.6, 6.1, 2.5}, {3.0, 6.5, 3.2, 5.1, 2.0}, {3.0, 6.4, 2.7, 5.3, 1.9}, {3.0, 6.8, 3.0, 5.5, 2.1}, {3.0, 5.7, 2.5, 5.0, 2.0}, {3.0, 5.8, 2.8, 5.1, 2.4}, {3.0, 6.4, 3.2, 5.3, 2.3}, {3.0, 6.5, 3.0, 5.5, 1.8}, {3.0, 7.7, 3.8, 6.7, 2.2}, {3.0, 7.7, 2.6, 6.9, 2.3}, {3.0, 6.0, 2.2, 5.0, 1.5}, {3.0, 6.9, 3.2, 5.7, 2.3}, {3.0, 5.6, 2.8, 4.9, 2.0}, {3.0, 7.7, 2.8, 6.7, 2.0}, {3.0, 6.3, 2.7, 4.9, 1.8}, {3.0, 6.7, 3.3, 5.7, 2.1}, {3.0, 7.2, 3.2, 6.0, 1.8}, {3.0, 6.2, 2.8, 4.8, 1.8}, {3.0, 6.1, 3.0, 4.9, 1.8}, {3.0, 6.4, 2.8, 5.6, 2.1}, {3.0, 7.2, 3.0, 5.8, 1.6}, {3.0, 7.4, 2.8, 6.1, 1.9}, {3.0, 7.9, 3.8, 6.4, 2.0}, {3.0, 6.4, 2.8, 5.6, 2.2}, {3.0, 6.3, 2.8, 5.1, 1.5}, {3.0, 6.1, 2.6, 5.6, 1.4}, {3.0, 7.7, 3.0, 6.1, 2.3}, {3.0, 6.3, 3.4, 5.6, 2.4}, {3.0, 6.4, 3.1, 5.5, 1.8}, {3.0, 6.0, 3.0, 4.8, 1.8}, {3.0, 6.9, 3.1, 5.4, 2.1}, {3.0, 6.7, 3.1, 5.6, 2.4}, {3.0, 6.9, 3.1, 5.1, 2.3}, {3.0, 5.8, 2.7, 5.1, 1.9}, {3.0, 6.8, 3.2, 5.9, 2.3}, {3.0, 6.7, 3.3, 5.7, 2.5}, {3.0, 6.7, 3.0, 5.2, 2.3}, {3.0, 6.3, 2.5, 5.0, 1.9}, {3.0, 6.5, 3.0, 5.2, 2.0}, {3.0, 6.2, 3.4, 5.4, 2.3}, {3.0, 5.9, 3.0, 5.1, 1.8} }; int irisResponseIdx = 0; DecisionTree.VariableType[] irisVarType = { DecisionTree.VariableType.CATEGORICAL, DecisionTree.VariableType.QUANTITATIVE_CONTINUOUS, DecisionTree.VariableType.QUANTITATIVE_CONTINUOUS, DecisionTree.VariableType.QUANTITATIVE_CONTINUOUS, DecisionTree.VariableType.QUANTITATIVE_CONTINUOUS }; int n = irisFisherData.length, m = irisFisherData[0].length; double[][] irisXY = new double[n][m]; double[] irisY = new double[n]; for (int i = 0; i < n; i++) { System.arraycopy(irisFisherData[i], 0, irisXY[i], 0, m); irisXY[i][0]--; irisY[i] = irisXY[i][0]; } RandomTrees rf = new RandomTrees(irisXY, irisResponseIdx, irisVarType); rf.setRandomObject(new Random(123457)); rf.fitModel(); double outOfBagError = rf.getOutOfBagPredictionError(); double[] outOfBagPredictions = rf.getOutOfBagPredictions(); int[][] rfClassErrors = rf.getClassErrors(irisY, outOfBagPredictions); System.out.println("RandomTrees ALACART out of bag error rate: " + outOfBagError + "\n"); new PrintMatrix("RandomTrees ALACART out of bag errors:"). print(rfClassErrors); } }
RandomTrees ALACART out of bag error rate: 0.04666666666666667 RandomTrees ALACART out of bag errors: 0 1 0 0 50 1 4 50 2 3 50 3 7 150Link to Java source.