Contents
What is multinomial naive Bayes classifier?
The multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial distribution normally requires integer feature counts. However, in practice, fractional counts such as tf-idf may also work.
What is Alpha in multinomial naive Bayes?
In Multinomial Naive Bayes, the alpha parameter is what is known as a hyperparameter; i.e. a parameter that controls the form of the model itself.
What is Laplace correction in naive Bayes?
Laplace smoothing is a smoothing technique that helps tackle the problem of zero probability in the Naïve Bayes machine learning algorithm. Using higher alpha values will push the likelihood towards a value of 0.5, i.e., the probability of a word equal to 0.5 for both the positive and negative reviews.
Why is multinomial Naive Bayes used?
The Naive Bayes algorithm has the following advantages: It is easy to implement as you only have to calculate probability. You can use this algorithm on both continuous and discrete data. It is simple and can be used for predicting real-time applications.
Why do we use Laplace correction?
Laplace smoothing It is introduced to solve the problem of zero probability i.e. when a query point contains a new observation, which is not yet seen in training data while calculating probabilities.
What makes naive Bayes classification so naive?
Naive Bayes is so ‘naive’ because it makes assumptions that are virtually impossible to see in real-life data and assumes that all the features are independent. Let’s take an example and implement the Naive Bayes Classifier, here we have a dataset that has been given to us and we’ve got a scatterplot which represents it.
What is the naive Bayes algorithm used for?
Naive Bayes is a probabilistic machine learning algorithm designed to accomplish classification tasks. It is currently being used in varieties of tasks such as sentiment prediction analysis, spam filtering and classification of documents etc.
When to use naive Bayes classifier?
Naive Bayes classifier is successfully used in various applications such as spam filtering, text classification, sentiment analysis, and recommender systems. It uses Bayes theorem of probability for prediction of unknown class.
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.