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How do I train Naive Bayes classifier in Matlab?
Train Naive Bayes Classifier Create a naive Bayes classifier for Fisher’s iris data set. Then, specify prior probabilities after training the classifier. Load the f isheriris data set. Create X as a numeric matrix that contains four petal measurements for 150 irises.
How good is the performance of Naive Bayes model?
Pros: It is easy and fast to predict class of test data set. When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data. It perform well in case of categorical input variables compared to numerical variable(s).
How to improve the accuracy of a naive Bayes classifier?
I have implemented a Naive Bayes Classifier, and with some feature selection (mostly filtering useless words), I’ve gotten about a 30% test accuracy, with 45% training accuracy. This is significantly better than random, but I want it to be better.
When to use naive Bayes instead of logistic regression?
Given that interactions between attributes are ignored in the model, we do not need examples of these interactions and therefore generally less data than other algorithms, such as logistic regression. Further, it is less likely to overfit the training data with a smaller sample size. Try Naive Bayes if you do not have much training data. 11.
How is missing data handled in naive Bayes?
Naive Bayes can handle missing data. Attributes are handled separately by the algorithm at both model construction time and prediction time. As such, if a data instance has a missing value for an attribute, it can be ignored while preparing the model, and ignored when a probability is calculated for a class value.
How are joint probabilities calculated in naive Bayes?
Probabilities are often small numbers. To calculate joint probabilities, you need to multiply probabilities together. When you multiply one small number by another small number, you get a very small number. It is possible to get into difficulty with the precision of your floating point values, such as under-runs.
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