What are the disadvantages of naive Bayesian classifier?

Disadvantages

• Naive Bayes assumes that all predictors (or features) are independent, rarely happening in real life.
• This algorithm faces the ‘zero-frequency problem’ where it assigns zero probability to a categorical variable whose category in the test data set wasn’t available in the training dataset.

How does naive Bayes classification algorithm work?

Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class. The class with the highest probability is considered as the most likely class.

What is naive Bayes algorithm in machine learning?

Naïve Bayes algorithm is a supervised learning algorithm, which is based on Bayes theorem and used for solving classification problems. Naïve Bayes Classifier is one of the simple and most effective Classification algorithms which helps in building the fast machine learning models that can make quick predictions.

What is the naive Bayes algorithm 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.

What are the pros and cons of naive Bayes classifier?

Pros and Cons of Naive Bayes Algorithm

• 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 the naive rule?

It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

What is Gaussian Naive Bayes classifier?

Gaussian Naive Bayes is a variant of Naive Bayes that follows Gaussian normal distribution and supports continuous data. Naive Bayes are a group of supervised machine learning classification algorithms based on the Bayes theorem. It is a simple classification technique, but has high functionality.

What is the main advantage of a Naive Bayes classifier compared to a decision tree?

Decision tree vs naive Bayes : Decision tree is a discriminative model, whereas Naive bayes is a generative model. Decision trees are more flexible and easy. Decision tree pruning may neglect some key values in training data, which can lead the accuracy for a toss.

What is the assumption of the Naive Bayes classifier?

What makes naive Bayes classification so naive?

What’s so naive about naive Bayes’? Naive Bayes (NB) is ‘naive’ because it makes the assumption that features of a measurement are independent of each other. This is naive because it is (almost) never true. Here is why NB works anyway. NB is a very intuitive classification algorithm.

What is intuitive explanation of naive Bayes classifier?

Naive Bayes classifiers have been especially popular for text classification, and are a traditional solution for problems such as spam detection. An intuitive explanation for the Maximum A Posteriori Probability MAP is to think probabilities as degrees of belief. For example, how likely are we vote for a candidate depends on our prior belief.

When to use naive Bayes?

Usually Multinomial Naive Bayes is used when the multiple occurrences of the words matter a lot in the classification problem. Such an example is when we try to perform Topic Classification. The Binarized Multinomial Naive Bayes is used when the frequencies of the words don’t play a key role in our classification.

Why is naive Bayesian classification called naive?

Naive Bayesian classification is called naive because it assumes class conditional independence. That is, the effect of an attribute value on a given class is independent of the values of the other attributes.