- Load your dataset into a matrix in MATLAB.
- Split the dataset into training and testing datasets. You can use the cvpartition function in MATLAB to split the data into a training and testing set, for example:
Data Analysis Matlab Code
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I have a dataset(145 rows, 32 columns without student id and attributes) https://archive.ics.uci.edu/ml/datasets/Higher+Education+Students+Performance+Evaluation+Dataset ). I can read and find the centered data matrix dataset. I even let the user select an attribute and find the mean, median, sum, max, range, skewness, kurtosis, boxplot, and the number of outliers. But I can't implement the naive Bayes Classifier algorithm.
i know how to do this algorithm i can't convert it to matlab code.
Ashutosh Bajpai on 17 Feb 2023
To implement the Naive Bayes Classifier algorithm in MATLAB, you can follow these steps:
cv = cvpartition(145,'HoldOut',0.3);
trainingData = data(cv.training,:);
testingData = data(cv.test,:);
3. Train the classifier using the training data. In MATLAB, you can use the fitcnb function to fit a Naive Bayes classifier to your data:
nb = fitcnb(trainingData(:,1:end-1), trainingData(:,end));
4. Evaluate the classifier using the testing data. In MATLAB, you can use the predict method to predict the class labels for the testing data:
predictions = predict(nb, testingData(:,1:end-1));
5. Calculate the performance metrics for the classifier. You can use the confusionmat function in MATLAB to calculate the confusion matrix, and then use the confusion matrix to calculate accuracy, precision, recall, and F1-score:
cm = confusionmat(testingData(:,end), predictions);
accuracy = sum(diag(cm))/sum(cm(:));
precision = diag(cm)./sum(cm,2);
recall = diag(cm)./sum(cm,1)';
f1_score = 2*(precision.*recall)./(precision+recall);
These are the basic steps to implement the Naive Bayes Classifier algorithm in MATLAB. You can use these steps as a starting point and modify them as needed for your specific use case.