Fisher's (1936) Iris data is often used for benchmarking classification algorithms. It consists of the following continuous input attributes and a classification target:
This example trains a Naive Bayes classifier using 140 of the 150 continuous patterns, then classifies ten unknown plants using their sepal and petal measurements.
import com.imsl.datamining.*; import com.imsl.stat.NormalDistribution; public class NaiveBayesClassifierEx1 { private static double[][] irisData = { {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, .2}, {1.0, 5.0, 3.2, 1.2, .2}, {1.0, 5.5, 3.5, 1.3, .2}, {1.0, 4.9, 3.6, 1.4, .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} }; public static void main(String[] args) throws Exception { /* Data corrections described in the KDD data mining archive */ irisData[34][4] = 0.1; irisData[37][2] = 3.1; irisData[37][3] = 1.5; /* Train first 140 patterns of the iris Fisher Data */ int[] irisClassificationData = new int[irisData.length - 10]; double[][] irisContinuousData = new double[irisData.length - 10][irisData[0].length - 1]; for (int i = 0; i < irisData.length - 10; i++) { irisClassificationData[i] = (int) irisData[i][0] - 1; System.arraycopy(irisData[i], 1, irisContinuousData[i], 0, irisData[0].length - 1); } int nNominal = 0; /* no nominal input attributes */ int nContinuous = 4; /* four continuous input attributes */ int nClasses = 3; /* three classification categories */ NaiveBayesClassifier nbTrainer = new NaiveBayesClassifier(nContinuous, nNominal, nClasses); for (int i = 0; i < nContinuous; i++) { nbTrainer.createContinuousAttribute(new NormalDistribution()); } nbTrainer.train(irisContinuousData, irisClassificationData); int[][] classErrors = nbTrainer.getTrainingErrors(); System.out.println(" Iris Classification Training Error Rates"); System.out.println("------------------------------------------------"); System.out.println(" Setosa Versicolour Virginica | Total"); System.out.println(" " + classErrors[0][0] + "/" + classErrors[0][1] + " " + classErrors[1][0] + "/" + classErrors[1][1] + " " + classErrors[2][0] + "/" + classErrors[2][1] + " | " + classErrors[3][0] + "/" + classErrors[3][1]); System.out.println( "------------------------------------------------\n\n\n"); /* Classify last 10 iris data patterns with the trained classifier */ double[] continuousInput = new double[(irisData[0].length - 1)]; double[] classifiedProbabilities = new double[nClasses]; System.out.println("Probabilities for Incorrect Classifications"); System.out.println(" Predicted "); System.out.println( " Class | Class | P(0) P(1) P(2) "); System.out.println( "-------------------------------------------------------"); for (int i = 0; i < 10; i++) { int targetClassification = (int) irisData[(irisData.length - 10) + i][0] - 1; System.arraycopy(irisData[(irisData.length - 10) + i], 1, continuousInput, 0, (irisData[0].length - 1)); classifiedProbabilities = nbTrainer.probabilities(continuousInput, null); int classification = nbTrainer.predictClass(continuousInput, null); if (classification == 0) { System.out.print("Setosa |"); } else if (classification == 1) { System.out.print("Versicolour |"); } else if (classification == 2) { System.out.print("Virginica |"); } else { System.out.print("Missing |"); } if (targetClassification == 0) { System.out.print(" Setosa |"); } else if (targetClassification == 1) { System.out.print(" Versicolour |"); } else if (targetClassification == 2) { System.out.print(" Virginica |"); } else { System.out.print(" Missing |"); } for (int j = 0; j < nClasses; j++) { Object[] pArgs = {new Double(classifiedProbabilities[j])}; System.out.printf(" %2.3f ", pArgs); } System.out.println(); } } }
The Naive Bayes classifier incorrectly classifies 6 of the 150 training patterns.
Iris Classification Training Error Rates ------------------------------------------------ Setosa Versicolour Virginica | Total 0/50 0/50 20/40 | 20/140 ------------------------------------------------ Probabilities for Incorrect Classifications Predicted Class | Class | P(0) P(1) P(2) ------------------------------------------------------- Virginica | Virginica | 0.000 0.436 0.564 Virginica | Virginica | 0.000 0.466 0.534 Versicolour | Virginica | 0.000 0.542 0.458 Virginica | Virginica | 0.000 0.441 0.559 Virginica | Virginica | 0.000 0.412 0.588 Virginica | Virginica | 0.000 0.466 0.534 Versicolour | Virginica | 0.000 0.542 0.458 Versicolour | Virginica | 0.000 0.515 0.485 Virginica | Virginica | 0.000 0.460 0.540 Versicolour | Virginica | 0.000 0.551 0.449Link to Java source.