Posted on 2022-05-27, by divlsngh.
These are the most well-known and most well-known machine-learning algorithms.
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Naive Bayes Algorithm to Classifiers (Supervised learning classification)
The Naive Bayes classifier is built upon the Bayes theorem that declares that each value is independent of any other value. It lets us decide on the category or class through a set of traits by using probabilities.
Despite its simplistic structure, the classifier will perform extremely well and is often used due to the fact that it can be superior to other more advanced classification methods.
K is a clustering algorithm (Unsupervised Learning and Clustering)
K is a short form for Clustering algorithm. It is an unsupervised method of learning that is employed to identify data that has not been categorized, i.e. data that does not have categorical category, or groups.
The algorithm detects groups within the data, and determines the number of groups found by numbers that is K. The algorithm performs several repetitions to allocate each item to one or more of the K groups based on the attributes provided by.
Support Vector Machine Algorithm (Supervised Learning Classification)
Support Vector Machine algorithms are programs which are supervised and analyze data to assist in classifying data as well as for regression analysis. They filter information into categorizing categories.
This is achieved by providing training examples, which are classifying them as belonging to one of two categories. The algorithm will develop a model that gives different values for one type and another.
Linear Regression (Supervised Learning/Regression)
Linear regressions are generally considered to be the easiest type of regression. Simple linear regression helps us understand the relation between non-continuous and continuous variables.
Logistic Regression (Supervised learning - Classification)
Logistic Regression is the method for determining the likelihood of an event occurring with the information that has been provided. It's used to identify the dependent variable binary in which there are just two variables, which are namely 0 , which are used to represent results.
Artificial Neural Networks (Reinforcement Learning)
Artificial neural networks (ANN) consist of elements that are laid out in layers. Each layer is connected to the layers that are on the opposite end.
They ANNs are affected by biological systems, such as neurons, and how they process information. They're essentially an array of interconnected processing components working to resolve specific problems.
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