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What is naive Bayes classification algorithm?

What is naive Bayes classification algorithm?

Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.

What is the naive Bayes algorithm used for?

Naive Bayes is a probabilistic machine learning algorithm that can be used in a wide variety of classification tasks. Typical applications include filtering spam, classifying documents, sentiment prediction etc. It is based on the works of Rev.

How is naive Bayes algorithm implemented?

Naive Bayes Tutorial (in 5 easy steps)

  1. Step 1: Separate By Class.
  2. Step 2: Summarize Dataset.
  3. Step 3: Summarize Data By Class.
  4. Step 4: Gaussian Probability Density Function.
  5. Step 5: Class Probabilities.

Why naïve Bayesian classification is called naïve describe naïve Bayesian classification with example?

Naive Bayes is called naive because it assumes that each input variable is independent. The thought behind naive Bayes classification is to try to classify the data by maximizing P(O | Ci)P(Ci) using Bayes theorem of posterior probability (where O is the Object or tuple in a dataset and “i” is an index of the class).

What is classification of algorithm?

The Classification algorithm is a Supervised Learning technique that is used to identify the category of new observations on the basis of training data. In Classification, a program learns from the given dataset or observations and then classifies new observation into a number of classes or groups.

Is naive Bayes used for classification?

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.

Why naive Bayes works with many number of 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.

What is classification algorithm?

What is the main idea of naïve Bayesian classification?

A naive Bayes classifier assumes that the presence (or absence) of a particular feature of a class is unrelated to the presence (or absence) of any other feature, given the class variable. Basically, it’s “naive” because it makes assumptions that may or may not turn out to be correct.