Table of Contents
- 1 Why correlated features affects performance in Naive Bayes?
- 2 Why does Naive Bayes work well with many features?
- 3 What is the correlate assumption for a Naive Bayes classifier?
- 4 How does a Naive Bayes classifier work?
- 5 Why is naive Bayes less accurate?
- 6 What makes naive Bayes classification so naive?
- 7 What is naive Bayes classification?
- 8 When to use naive Bayes?
The performance of Naive Bayes can degrade if the data contains highly correlated features. This is because the highly correlated features are voted for twice in the model, over inflating their importance.
Why does Naive Bayes work well with many features?
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.
Does Multicollinearity affect Naive Bayes?
Answer: Multi collinearity is a condition when two or more variables carry almost the same information. So, multi collinearity does not affect the Naive Bayes.
What is the correlate assumption for a Naive Bayes classifier?
Naive Bayes Classifier belongs to the family of probabilistic classifiers and is based on Bayes’ theorem. It is based on the assumption that the presence of one feature in a class is independent to the other feature present in the same class.
How does a Naive Bayes classifier work?
The Naive Bayes classifier works on the principle of conditional probability, as given by the Bayes theorem. While calculating the math on probability, we usually denote probability as P. Some of the probabilities in this event would be as follows: The probability of getting two heads = 1/4.
Why is Naive Bayes less accurate?
The assumption that all features are independent is not usually the case in real life so it makes naive bayes algorithm less accurate than complicated algorithms.
Why is naive Bayes less accurate?
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.
Why is naive Bayes 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.
What is naive Bayes classification?
A naive Bayes classifier is an algorithm that uses Bayes’ theorem to classify objects. Naive Bayes classifiers assume strong, or naive, independence between attributes of data points. Popular uses of naive Bayes classifiers include spam filters, text analysis and medical diagnosis.
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.