Contents
Is Naive Bayes bad if yes under what aspects?
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. And this will get a zero when all the probabilities are multiplied.
What can Naive Bayes be used for?
Naive Bayes is a probabilistic machine learning algorithm that can be used in a wide variety of classification tasks. Typical applications include filtering spam, classifying documents, sentiment prediction etc. It is based on the works of Rev.
Which is faster naive Bayes or normal Bayes?
The Naive Bayes classifier is much faster with its probability calculations. This is the kind of algorithm used when all features follow a normal distribution. All features are continuous valued. The assumption is that there is no covariance between the independent features.
Which is a better classifier naive Bayes or logistic regression?
If the data set follows the bias then Naive Bayes will be a better classifier. Both Naive Bayes and Logistic regression are linear classifiers, Logistic Regression makes a prediction for the probability using a direct functional form where as Naive Bayes figures out how the data was generated given the results.
This kind of a problem tells us to go for some classification algorithms like Logistic Regression, Tree Based Algorithms, Support Vector Machines, Naive Bayes etc. When you actually get to work with the above algorithms, Naive Bayes gives you the best kind of results which are desired.
When to use random forest or naive Bayes?
The gap between Random Forest and Naive Bayes is very large, but I shall answer the question in simple terms. Naive Bayes is very useful when the features are “counts” based or “discrete measurements” based, where the features are independent of each other.