Naive Bayes is a probabilistic machine learning algorithm that is based on the Bayes Theorem and is used for a wide range of classification challenges.
In this blog, we will learn about the Naive Bayes algorithm and all of its core concepts so that there are no gaps in the information.
Machine Learning – Discriminative & Generative Model
As we all know, machine learning is the technology that predicts goal B using characteristics A, i.e., computing the conditional probability P(B|A).
Then, for the discriminative model, we only take into account assessing the conditional probability. This establishes the classiﬁer under the condition of a limited sample, without evaluating the sample’s generative model, instead of learning the prediction model, like in the binary classification problem.
After obtaining P(B|A) from the generative model, we must discover the shared distribution probability between A and B, and the major win is to make 2 joint probability distributions. Choose the bigger one.
Introduction to the Naive Bayes Algorithm
Firstly, Naive Bayes is a generative model.
Naive Bayes is a supervised machine learning technique that is mostly used for classification. In this case, ‘supervised’ implies that the algorithm has been trained using both input characteristics and category outputs. But why is it termed the Naive Bayes Algorithm?
The Naive Bayes classifier assumes that the existence of a specific feature in a class is unrelated to the presence of any other feature. Or that the influence of an attribute value on a specific class is unrelated to the presence of the other attributes.
The approach is simple to use & is especially beneficial for big datasets. Along with its simplicity, it is known to outperform even the most complex categorization systems.
Before delving into the details of this algorithm, it’s crucial to know the Bayes theorem & conditional probability, as the algorithm operates on the latter’s concept.
What is Bayes Theorem?
The Bayes’ Theorem is a straightforward mathematical procedure for estimating conditional probabilities.
Conditional probability is a metric of the probability of one event occurring, provided that another event has already occurred (through assumption, presumption, statement, or evidence).
Aside from statistics, the Bayes’ theorem is applied in a variety of areas, the most noteworthy of which are medicine and pharmacology. Furthermore, the theory is widely used in several disciplines of finance. Some of the applications involve but are not restricted to estimating the risk of lending loans to consumers or projecting the probability of an investment’s success.
Bayes Theorem Formula –
P(A ∣B) = P(B |A) . P(A) / P(B )
This formula tells us how frequently A occurs if B occurs, denoted by P(A|B), also known as posterior probability. When we know: how frequently B occurs if A occurs, written P(B|A), and how probable A is on its own, written P(A), and how probable B is on its own, stated P(A) (B).
In layman’s words, Bayes’ Theorem is a method for determining a probability while we know other probabilities.
Naive Bayes – Assumptions
The key premise of Nave Bayes is that each feature creates an: independent & equal contribution to the result.
Let us use an example to have a better understanding. Consider the automobile theft issue, which has the characteristics of Color, Type, Location, and the aim – Stolen may be chosen Yes or No.
Naive Bayes – Example
If a patient is an alcoholic, you might be curious about their chances of developing liver disease. “Being an alcoholic” is the litmus test for liver damage.
A might indicate the occurrence “Patient has a liver illness.” According to previous data, 10% of patients who join your hospital have liver problems. P(A) = 0.10.
B might stand for the litmus test, “Patient is an alcoholic.” Alcoholism affects 5% of the hospital’s patients. P(B) equals 0.05.
You may also be aware that 7% of persons diagnosed with liver problems are alcoholics. This is your B|A: a patient with liver illness has a 7% chance of being an alcoholic.
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Types of Naive Bayes Classifiers
- Multinomial Naive Bayes Classifier
Feature vectors represent the frequency with which particular events were created by a multinomial distribution. This is the event model that is most commonly used for document categorization.
- Bernoulli Naive Bayes Classifier
Features in the multivariate Bernoulli event model are independent booleans, i.e., binary variables, that describe inputs. This model, much like the multinomial model, is widely popular for document classification problems in which binary term occurrence (i.e., whether a word appears in a document or not) characteristics are utilized rather than term frequencies (i.e., frequency of a word in the document).
- Gaussian Naive Bayes Classifier
Continuous values linked with each feature are expected to follow a Gaussian distribution in Gaussian Nave Bayes – Normal distribution. When plotted, it produces a bell-shaped curve that is symmetrical about the mean of the attribute values.
Naive Bayes – Pros and Cons
- On small-scale data, it performs wonderfully
- Appropriate for multi-classification jobs
- Suitable for gradual training
- The expression form of the incoming data is quite important (discrete, continuous, max & min values, etc.)
Amongst machine learning classification methods, Naive Bayes is distinct from the majority of others. Most classification algorithms, like decision trees, logistic regression, KNN, support vector machines, and so on, are discriminative techniques. This means they establish the connection between feature output Y and feature X directly, or decision function, or conditional distribution. Most classification algorithms, like decision trees, logistic regression, KNN, support vector machines, and so on, are discriminative techniques.
However, Naive Bayes is a generic approach in which the joint distribution of feature output and feature is directly found and then inferred is used. Naive Bayes is fairly intuitive & does not need a lot of math. It has several uses in a variety of disciplines.
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