if (trainRoot.findBestSplit()) { nextSplits.add(trainRoot);
Node split = findBestSplit(n, count, falseCount, impurity, variables[j]); if (split.splitScore > node.splitScore) { node.splitFeature = split.splitFeature; Node split = findBestSplit(n, count, falseCount, impurity, variables[j]); if (split.splitScore > node.splitScore) { node.splitFeature = split.splitFeature;
if (tc > nodeSize && trueChild.findBestSplit()) { if (nextSplits != null) { nextSplits.add(trueChild); if (fc > nodeSize && falseChild.findBestSplit()) { if (nextSplits != null) { nextSplits.add(falseChild);
@Override public Node call() { // An array to store sample count in each class for false child node. int[] falseCount = new int[k]; return findBestSplit(n, count, falseCount, impurity, j); } }