Is there any limitation issue with Naive Bayes method for classification?

The zero-frequency problem One of the disadvantages of Naïve-Bayes is that if you have no occurrences of a class label and a certain attribute value together then the frequency-based probability estimate will be zero. This is how we’ll get rid of getting a zero probability.

Does Naive Bayes work with numerical data?

Naive Bayes is a classifier and will therefore perform better with categorical data. Although numeric data will also suffice, it assumes all numeric data are normally distributed which is unlikely in real world data.

Can naive Bayes be used for multiclass classification?

Pros: It is easy and fast to predict class of test data set. It also perform well in multi class prediction. When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data.

Can Naive Bayes be used for multiclass classification?

Can Naive Bayes use categorical data?

The categorical Naive Bayes classifier is suitable for classification with discrete features that are categorically distributed. The categories of each feature are drawn from a categorical distribution.

Is naive Bayes good for high dimensional data?

Because of the class independence assumption, naive Bayes classifiers can quickly learn to use high dimensional features with limited training data compared to more sophisticated methods. This can be useful in situations where the dataset is small compared to the number of features, such as images or texts.

Can SVM be used for multiclass classification?

In its most basic type, SVM doesn’t support multiclass classification. For multiclass classification, the same principle is utilized after breaking down the multi-classification problem into smaller subproblems, all of which are binary classification problems.

Which algorithm is best for multiclass classification?

Popular algorithms that can be used for multi-class classification include:

• k-Nearest Neighbors.
• Decision Trees.
• Naive Bayes.
• Random Forest.
• Gradient Boosting.