How to produce a confusion matrix and find the misclassification rate of the Naive Bayes Classifier in R? Last Updated : 23 Jul, 2025 Comments Improve Suggest changes Like Article Like Report Producing a confusion matrix and calculating the misclassification rate of a Naive Bayes Classifier in R involves a few straightforward steps. In this guide, we'll use a sample dataset to demonstrate how to interpret the results.Understanding Confusion MatrixA confusion matrix is a table that describes the performance of a classification model by summarizing the counts of true positive (TP), true negative (TN), false positive (FP), and false negative (FN) predictions made by the model.Misclassification RateThe misclassification rate is a metric that measures the overall error rate of a classification model. This rate quantifies the proportion of incorrect predictions made by the model.Let's apply the Naïve Bayes classifier to the famous Iris dataset to demonstrate how to compute a confusion matrix and Step-by-Step Implementation to calculate the misclassification rate in R Programming Language.Step 1: Load Required Libraries and DatasetFirst, install and load necessary packages. R # Install and load necessary packages install.packages("e1071") install.packages("caret") library(e1071) library(caret) Step 2: Load and Prepare DatasetFor this example, we'll use the built-in iris dataset, which is commonly used for classification tasks. R # Load the dataset data(iris) # Inspect the structure of the dataset head(iris) Output: Sepal.Length Sepal.Width Petal.Length Petal.Width Species1 5.1 3.5 1.4 0.2 setosa2 4.9 3.0 1.4 0.2 setosa3 4.7 3.2 1.3 0.2 setosa4 4.6 3.1 1.5 0.2 setosa5 5.0 3.6 1.4 0.2 setosa6 5.4 3.9 1.7 0.4 setosaStep 3: Split Data into Training and Testing SetsSplit the dataset into training and testing sets. We'll use 70% of the data for training and 30% for testing. R set.seed(123) # Set seed for reproducibility trainIndex <- createDataPartition(iris$Species, p = 0.7, list = FALSE) data_train <- iris[trainIndex, ] data_test <- iris[-trainIndex, ] Step 4: Train Naïve Bayes ClassifierTrain the Naive Bayes classifier using the training data. R # Train Naïve Bayes classifier model <- naiveBayes(Species ~ ., data = data_train) Step 5: Predict Classes on Test Data and Produce Confusion MatrixUse the trained model to predict classes on the test data. R # Create confusion matrix conf_matrix <- confusionMatrix(data = predicted_class, reference = data_test$Species) conf_matrix Output:Confusion Matrix and Statistics ReferencePrediction setosa versicolor virginica setosa 15 0 0 versicolor 0 13 2 virginica 0 2 13Overall Statistics Accuracy : 0.9111 95% CI : (0.7878, 0.9752) No Information Rate : 0.3333 P-Value [Acc > NIR] : 8.467e-16 Kappa : 0.8667 Mcnemar's Test P-Value : NA Statistics by Class: Class: setosa Class: versicolor Class: virginicaSensitivity 1.0000 0.8667 0.8667Specificity 1.0000 0.9333 0.9333Pos Pred Value 1.0000 0.8667 0.8667Neg Pred Value 1.0000 0.9333 0.9333Prevalence 0.3333 0.3333 0.3333Detection Rate 0.3333 0.2889 0.2889Detection Prevalence 0.3333 0.3333 0.3333Balanced Accuracy 1.0000 0.9000 0.9000Step 6: Calculate Misclassification RateCompute the misclassification rate from the confusion matrix. R # Misclassification rate misclassification_rate <- 1 - conf_matrix$overall['Accuracy'] cat("Misclassification Rate:", misclassification_rate, "\n") Output:Misclassification Rate: 0.08888889 ConclusionBy following these steps, you can produce a confusion matrix and calculate the misclassification rate for a Naïve Bayes Classifier in R. This process allows you to assess how well the classifier performs in predicting classes based on the test data. The confusion matrix provides insights into true positives, true negatives, false positives, and false negatives, while the misclassification rate quantifies the overall error rate of the classifier. Adjustments in training and model evaluation can further refine the classifier's performance for specific classification tasks. 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