Naive Bayes | Statistics
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The naïve bayes classifier uses Bayes's theorem to estimate probabilities of events based on independent features, making it a simple yet effective probabilistic classifier. It has applications in various fields, including medical diagnosis and text categorization, and can operate on different types of data through implementations such as Gaussian, multinomial, and Bernoulli naïve bayes. The classifier is popular for its efficiency, requiring minimal training data and is implemented in multiple programming environments, including Weka, R, and Python.
Naive Bayes | Statistics
- 1. Naïve bayes
- 2. Introduction The naïve bayes classifier is basically a classifier that uses the bayes’s theorem. According, to the statistics and probability and probability theory, the bayes’s theorem is used to describe the probability for an event to occur that is based on the conditions that we think that might be related to the event that occur It is just an assumption that is made based on the independent character among the predictors The classifier assumes that, in a given class of several factors, the presence of a particular factor is always unrelated to the any other factor that is present in the same class The naïve bayes classifier is classified under the probabilistic classifier that is simple by making assumptions that are independent of features and it is closely related to the machine learning
- 3. Overview The study regarding the naïve bayes started early since the 1950s extensively under the category of text retrieval in 1960s as it remained as one of the popular method that laid the base for the text categorization. This classifier helped in the judgment process which involved the categorization of documents by distinguishing them in the various factors They are estimated and distinguished by means of the word frequency as a factor. With the help of supporting methods such as the vector machine, it finds competitive atmosphere in many domains along with the pre-processing process Hence, these types of classifiers find application in the field of medical diagnosis that can be made automatic. They are called in many names such as simple classifier and independent classifier because of their functionality in the computer science and statistics
- 4. Naïve bayes In simple, naïve bayes is an easy technique for the construction of classifier models that are used to assign class labels for the instant problem that can be represented in the form of vectors that can include the feature values and these class labels are driven from the finite set of values. The naïve bayes is not a single algorithm but a combination or collection of algorithms that are categorized under a common purpose and under a common principle The concept behind the naïve bayes is that each and every factor in a class is independent of the other factors in the same class. A simple example for this type of classifier can be taken as apple where the factors like red (color), round (shape) and 10m (diameter) are considered and the classifier takes these factors as an independent factor and finds the probability that it is really an apple regarding the possible relations of those factors which are taken under consideration
- 5. Naïve bayes Naïve bayes classifier works well on the complex environments apparently with the help of the simplified assumptions. One of the experiments can be said as the analysis that is taken in 2004 It revealed the theoretical reasons of the efficiency of bayes classifier and also compared the approaches of fields called boosted trees and the forests. It requires minimum training data for the estimation of the values that are used for the classification and it is one of the major advantages The naïve bayes classifier uses conditional probability models were they are represented by means of vector quantity and n for the independent variables. This model applies instant probabilities for each possible outcome that occur in a given class The decision rile method is applied along with the probabilistic model. For the parameter and event models representation, we can use the popular method like multinomial and Bernoulli distribution that is based on the Bernoulli theorem
- 6. Features Bayes classifiers are scalable and hence require a number of linear parameter that can be obtained from the number of variables that are available in the learning method of problem For this type of learning method, many naïve bayes tutorial has been developed that are used for the training of the maximum-likelihood that is used in the EM algorithm and is done by the closed form of expressions which are linear in type They are iterative methods of approximation that is used by various types of classifiers
- 7. Types of naïve bayes implementations Gaussian naïve bayes: For the continuous data, we make use of the gaussian naïve bayes in which the assumption is made along the continuous values that are associated with each value based on the Gaussian distribution methods. Multinomial naïve bayes: This type of naïve bayes process is used and suggested for the samples that represent the frequencies which occur in certain events that are obtained by means of multinomial process. This type of multinomial naïve bayes classifier is said as linear classifier is always expressed in terms of log-space and Laplace transform process for the smoothing of the naïve bayes. Bernoulli naïve bayes: In a multivariate model, the independent Boolean equations describe the inputs. Like multinomial model, Bernoulli naïve model is also one of the popular tools for the documentation of classification of the various tasks which consists of occurrence of binary term other than frequencies.
- 8. Weka naïve bayes Weka is open source software that is used in the weka naïve bayes. This software is used in the process of data mining and in machine learning technique. The weka stands for Waikato environment for knowledge analysis (Weka) that is one of the popular tools used for the learning purpose that is originally written in java and developed in New Delhi.
- 9. Naïve bayes implementations Naïve bayes classifier is implemented in various softwares. Now, let’s see how the naïve bayes classification in R There are various packages available in R software. Caret is a very nice package that is used for data mining process Naïve bayes classification in python is also a more useful tool for the naïve bayes classifier implementation It is written in the programming language which is too common. This tool uses the statistical methods that are used to classify the words in a document that appear on the system The most common observation of this type of classification is observed in the spam filters that are used in the email designing
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