PROBABILISTIC MODELS OF ARTIFICIAL INTELLIGENCE
Introduction
The application of statistical analysis codes to data analysis is known as probabilistic models in
machine learning. It was one of the earliest approaches to artificial intelligence. Even now, it's still
used quite a bit. The Naive Bayes algorithm is one of the group's most well-known algorithms
Baye’s Theorem
Bayes' theorem is also known as Bayes' rule, Bayes' law, or Bayesian reasoning, which
determines the probability of an event with uncertain knowledge.
Bayes' theorem was named after the British mathematician Thomas Bayes. The Bayesian
inference is an application of Bayes' theorem, which is fundamental to Bayesian statistics.
It is a way to calculate the value of P(B|A) with the knowledge of P(A|B).
P(A|B) is known as posterior, which we need to calculate and it will be read as Probability of
hypothesis A when we have evidence B.
P(B|A) is called the likelihood, in which we consider that the hypothesis is true and then we
calculate the probability of evidence.
P(A) is called the prior probability, the probability of the hypothesis before considering the
evidence
P(B) is called marginal probability, pure probability of evidence
Naïve Bayes Classifier
The Naïve Bayes algorithm is a supervised learning method for classification issues that is
based on the Bayes theorem.
It is mostly employed in text categorization with a large training set
Being a probabilistic classifier, it makes predictions based on the likelihood that an object will
occur
Some popular examples of the Naïve Bayes Algorithm are spam filtration, Sentiment analysis
and classifying articles.
Naïve: Because it presumes that the occurrence of one trait is unrelated to the occurrence of
other features, it is known as Naïve. A red, spherical, sweet fruit, for instance, is recognized as
an apple if the fruit is identified based on its color, form and flavor.
Bayes: It is called Bayes because it depends on the principle of Bayes' Theorem
Advantages
Naïve Bayes is a quick and simple machine learning approach to forecast a class of datasets
Both binary and multi-class classifications can be done with it
In comparison to the various Approaches, it outperforms in multi-class forecasts
It is the most popular choice for text classification problems
© 2024 Athena Global Education. All Rights Reserved
Introduction
The application of statistical analysis codes to data analysis is known as probabilistic models in
machine learning. It was one of the earliest approaches to artificial intelligence. Even now, it's still
used quite a bit. The Naive Bayes algorithm is one of the group's most well-known algorithms
Baye’s Theorem
Bayes' theorem is also known as Bayes' rule, Bayes' law, or Bayesian reasoning, which
determines the probability of an event with uncertain knowledge.
Bayes' theorem was named after the British mathematician Thomas Bayes. The Bayesian
inference is an application of Bayes' theorem, which is fundamental to Bayesian statistics.
It is a way to calculate the value of P(B|A) with the knowledge of P(A|B).
P(A|B) is known as posterior, which we need to calculate and it will be read as Probability of
hypothesis A when we have evidence B.
P(B|A) is called the likelihood, in which we consider that the hypothesis is true and then we
calculate the probability of evidence.
P(A) is called the prior probability, the probability of the hypothesis before considering the
evidence
P(B) is called marginal probability, pure probability of evidence
Naïve Bayes Classifier
The Naïve Bayes algorithm is a supervised learning method for classification issues that is
based on the Bayes theorem.
It is mostly employed in text categorization with a large training set
Being a probabilistic classifier, it makes predictions based on the likelihood that an object will
occur
Some popular examples of the Naïve Bayes Algorithm are spam filtration, Sentiment analysis
and classifying articles.
Naïve: Because it presumes that the occurrence of one trait is unrelated to the occurrence of
other features, it is known as Naïve. A red, spherical, sweet fruit, for instance, is recognized as
an apple if the fruit is identified based on its color, form and flavor.
Bayes: It is called Bayes because it depends on the principle of Bayes' Theorem
Advantages
Naïve Bayes is a quick and simple machine learning approach to forecast a class of datasets
Both binary and multi-class classifications can be done with it
In comparison to the various Approaches, it outperforms in multi-class forecasts
It is the most popular choice for text classification problems
© 2024 Athena Global Education. All Rights Reserved
