Sage-Tips

Is naive Bayes bad if yes under what aspects?

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. And this will get a zero when all the probabilities are multiplied.

What is the accuracy of naive Bayes algorithm used for classification?

The accuracy matches the expected value calculated by the probability framework of 75\% and the composition of the training dataset. This majority class naive classifier is the method that should be used to calculate a baseline performance on your classification predictive modeling problems.

Can naive Bayes generate labels?

This is a supervised category of an algorithm. Which means we train the algorithm with given input records with known labels, make model and then apply the created model on unknown records to correctly classify them in given category. Since examples are the best to get familiar with any new algorithm.

Why is Naive Bayes good for text classification?

Since a Naive Bayes text classifier is based on the Bayes’s Theorem, which helps us compute the conditional probabilities of occurrence of two events based on the probabilities of occurrence of each individual event, encoding those probabilities is extremely useful.

What are the pros and cons of using Naive Bayes?

Pros and Cons of Naive Bayes Algorithm Pros: The assumption that all features are independent makes naive bayes algorithm very fast compared to complicated algorithms. In some cases, speed is preferred over higher accuracy. It works well with high-dimensional data such as text classification, email spam detection.

What is Naive Bayes used for?

Naive Bayes uses a similar method to predict the probability of different class based on various attributes. This algorithm is mostly used in text classification and with problems having multiple classes.

Is naive Bayes a good classifier?

Results show that Naïve Bayes is the best classifiers against several common classifiers (such as decision tree, neural network, and support vector machines) in term of accuracy and computational efficiency.

What is naive Bayes used for?

Is Naive Bayes classifier good?

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.

What is naive Bayes classifier in machine learning?

Naive Bayes classifier is a straightforward and powerful algorithm for the classification task. Even if we are working on a data set with millions of records with some attributes, it is suggested to try Naive Bayes approach.

What is nanaive Bayes algorithm?

Naive Bayes is a simple and effective machine learning algorithm for solving multi-class problems. It finds uses in many prominent areas of machine learning applications such as sentiment analysis and text classification.

What is multimultinomial naive Bayes used for?

MultiNomial Naive Bayes is preferred to use on data that is multinomially distributed. It is one of the standard classic algorithms. Which is used in text categorization (classification). Each event in text classification represents the occurrence of a word in a document.

How do we use naive Bayes in real life?

We use a similar method in Naive Bayes to give the probability of different class and then label it with the class having maximum probability. Let’s take an example, where we want to tell if a fruit is tomato or not. We can tell it’s a tomato from it’s shape, color and diameter (size). Tomato is red, it’s round and has about 9-10 cm diameter.