How is naive Bayes algorithm used for classification?
What is Naive Bayes algorithm? 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.
Can we use naive Bayes for binary classification?
Classification Problems: Naive Bayes is a classification algorithm suitable for binary and multiclass classification.
How is Laplace smoothing used in naive Bayes algorithm?
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.
What’s the difference between Laplace smoothing and Additive smoothing?
4. While in the general case it is often called Lidstone smoothing. Note: In statistics, additive smoothing, also called Laplace smoothing or Lidstone smoothing, is a technique used to smooth categorical data.
When to use multinomial naive Bayes in text classification?
Multinomial Naive Bayes ¶ MultinomialNB implements the naive Bayes algorithm for multinomially distributed data, and is one of the two classic naive Bayes variants used in text classification (where the data are typically represented as word vector counts, although tf-idf vectors are also known to work well in practice).
How is the probability calculated in Laplace smoothing?
In Laplace smoothing, 1 (one) is added to all the counts, and thereafter, the probability is calculated. This is one of the most trivial smoothing techniques out of all the techniques. ‘α’ should not disturb the uniform probabilities that are assigned to unknown data/new observations.