This example builds a random forest with ALACART decision trees. A single tree and the random forest are fit to the Kyphosis data and predictions for a test-data set consisting of 10 "new" cases are generated.
The Kyphosis data 81 cases represent 81 children who have undergone surgery to correct a type of spinal deformity known as Kyphosis. The response variable is the presence or absence of Kyphosis after the surgery. The three predictors are:
import com.imsl.math.*;
import com.imsl.stat.*;
import com.imsl.datamining.decisionTree.*;
public class RandomTreesEx1 {
public static void main(String[] args) throws Exception {
DecisionTree.VariableType[] kyphosisVarType = {
DecisionTree.VariableType.CATEGORICAL,
DecisionTree.VariableType.QUANTITATIVE_CONTINUOUS,
DecisionTree.VariableType.QUANTITATIVE_CONTINUOUS,
DecisionTree.VariableType.QUANTITATIVE_CONTINUOUS
};
int kyphosisResponseIdx = 0;
double[][] kyphosisXY = {
{0, 71, 3, 5}, {0, 158, 3, 14}, {1, 128, 4, 5}, {0, 2, 5, 1},
{0, 1, 4, 15}, {0, 1, 2, 16}, {0, 61, 2, 17}, {0, 37, 3, 16},
{0, 113, 2, 16}, {1, 59, 6, 12}, {1, 82, 5, 14}, {0, 148, 3, 16},
{0, 18, 5, 2}, {0, 1, 4, 12}, {0, 168, 3, 18}, {0, 1, 3, 16},
{0, 78, 6, 15}, {0, 175, 5, 13}, {0, 80, 5, 16}, {0, 27, 4, 9},
{0, 22, 2, 16}, {1, 105, 6, 5}, {1, 96, 3, 12}, {0, 131, 2, 3},
{1, 15, 7, 2}, {0, 9, 5, 13}, {0, 8, 3, 6}, {0, 100, 3, 14},
{0, 4, 3, 16}, {0, 151, 2, 16}, {0, 31, 3, 16}, {0, 125, 2, 11},
{0, 130, 5, 13}, {0, 112, 3, 16}, {0, 140, 5, 11}, {0, 93, 3, 16},
{0, 1, 3, 9}, {1, 52, 5, 6}, {0, 20, 6, 9}, {1, 91, 5, 12},
{1, 73, 5, 1}, {0, 35, 3, 13}, {0, 143, 9, 3}, {0, 61, 4, 1},
{0, 97, 3, 16}, {1, 139, 3, 10}, {0, 136, 4, 15}, {0, 131, 5, 13},
{1, 121, 3, 3}, {0, 177, 2, 14}, {0, 68, 5, 10}, {0, 9, 2, 17},
{1, 139, 10, 6}, {0, 2, 2, 17}, {0, 140, 4, 15}, {0, 72, 5, 15},
{0, 2, 3, 13}, {1, 120, 5, 8}, {0, 51, 7, 9}, {0, 102, 3, 13},
{1, 130, 4, 1}, {1, 114, 7, 8}, {0, 81, 4, 1}, {0, 118, 3, 16},
{0, 118, 4, 16}, {0, 17, 4, 10}, {0, 195, 2, 17}, {0, 159, 4, 13},
{0, 18, 4, 11}, {0, 15, 5, 16}, {0, 158, 5, 14}, {0, 127, 4, 12},
{0, 87, 4, 16}, {0, 206, 4, 10}, {0, 11, 3, 15}, {0, 178, 4, 15},
{1, 157, 3, 13}, {0, 26, 7, 13}, {0, 120, 2, 13}, {1, 42, 7, 6},
{0, 36, 4, 13}
};
double[][] kyphosisXYTest = {
{0, 71, 3, 5}, {1, 128, 4, 5}, {0, 1, 4, 15}, {0, 61, 6, 10},
{0, 113, 2, 16}, {1, 82, 5, 14}, {0, 148, 3, 16}, {0, 1, 4, 12},
{0, 1, 3, 16}, {0, 175, 5, 13}
};
ALACART dt
= new ALACART(kyphosisXY, kyphosisResponseIdx, kyphosisVarType);
dt.fitModel();
double[] singlePredictions = dt.predict(kyphosisXYTest);
RandomTrees rf = new RandomTrees(dt);
rf.setRandomObject(new Random(123457));
rf.setNumberOfRandomFeatures(2);
double[] rfPredictions = rf.predict(kyphosisXYTest);
new PrintMatrix("Kyphosis test data single tree predictions"
+ " on the test data:").print(singlePredictions);
new PrintMatrix("Kyphosis test data random forest predictions"
+ " on the test data:").print(rfPredictions);
}
}
Kyphosis test data single tree predictions on the test data: 0 0 0 1 0 2 0 3 1 4 0 5 0 6 0 7 0 8 0 9 0 Kyphosis test data random forest predictions on the test data: 0 0 0 1 1 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0Link to Java source.