Decision Trees

import { loadIris } from "deepbox/datasets";
import { accuracy } from "deepbox/metrics";
import { DecisionTreeClassifier } from "deepbox/ml";
import { trainTestSplit } from "deepbox/preprocess";

const iris = loadIris();
const [XTrain, XTest, yTrain, yTest] = trainTestSplit(
  iris.data, 
  iris.target, 
  {
    testSize: 0.2,
    randomState: 42,
  }
);

// Create and train decision tree
const dtc = new DecisionTreeClassifier({ 
  maxDepth: 5, 
  minSamplesSplit: 2 
});
dtc.fit(XTrain, yTrain);

const dtcPred = dtc.predict(XTest);
const acc = accuracy(yTest, dtcPred);

console.log("Decision Tree Classifier");
console.log(`Accuracy: ${(Number(acc) * 100).toFixed(2)}%`);

Decision Tree Classifier
Accuracy: 96.67%

import { mse, r2Score } from "deepbox/metrics";
import { DecisionTreeRegressor } from "deepbox/ml";
import { tensor } from "deepbox/ndarray";

// Create regression dataset
const XReg = tensor([
  [1, 2], [2, 3], [3, 4], [4, 5], [5, 6],
  [6, 7], [7, 8], [8, 9], [9, 10], [10, 11],
  [1, 3], [2, 5], [3, 2], [4, 1], [5, 4],
  [6, 3], [7, 6], [8, 5], [9, 8], [10, 7],
]);
const yReg = tensor([5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 7, 12, 7, 6, 13, 12, 19, 18, 25, 24]);

const [XRegTrain, XRegTest, yRegTrain, yRegTest] = trainTestSplit(
  XReg, 
  yReg, 
  {
    testSize: 0.2,
    randomState: 42,
  }
);

// Train decision tree regressor
const dtr = new DecisionTreeRegressor({ maxDepth: 5 });
dtr.fit(XRegTrain, yRegTrain);

const dtrPred = dtr.predict(XRegTest);
console.log("\nDecision Tree Regressor");
console.log(`MSE: ${mse(yRegTest, dtrPred).toFixed(4)}`);
console.log(`R²:  ${r2Score(yRegTest, dtrPred).toFixed(4)}`);

Decision Tree Regressor
MSE: 0.5000
R²:  0.9850

// Shallow tree (less overfitting)
const shallowTree = new DecisionTreeClassifier({ 
  maxDepth: 3,
  minSamplesSplit: 10 
});

// Deep tree (more expressive)
const deepTree = new DecisionTreeClassifier({ 
  maxDepth: 10,
  minSamplesSplit: 2 
});