Classification of Data Visualization & its Type
Classification is a process of finding a function which helps in dividing the data set into classes based on different parameters.
In classification, a computer program is trained on the training data set and based on that training, it categories that data into different classes.
The task of the classification algorithm is to find the mapping function to map the input (x) to the discrete output(y).
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| Data visualization (Data Algorithm) |
Classification algorithm can be divided into the following types :-
(i) Logistic Regression
(ii) K-Nearest Neighbors
(iii) Support Vector Machines
(iv) Kernel SVM
(v) Naïve Bayes
(vi) Decision Tree Classification
(vii) Random Forest Classification
(I) Logistic Regression :-
This article discusses the basics of Logistic Regression and its implementation in Python. Logistic regression is basically a supervised classification algorithm. In a classification problem, the target variable(or output), y, can take only discrete values for a given set of features(or inputs), X.
Contrary to popular belief, logistic regression IS a regression model. The model builds a regression model to predict the probability that a given data entry belongs to the category numbered as “1”. Just like Linear regression assumes that the data follows a linear function, Logistic regression models the data using the sigmoid function.
(ii) K-Nearest Neighbor's :-
K-Nearest Neighbors is one of the simplest Machine Learning algorithms based on Supervised Learning technique.
K-NN algorithm assumes the similarity between the new case/data and available cases and put the new case into the category that is most similar to the available categories.
K-NN algorithm stores all the available data and classifies a new data point based on the similarity. This means when new data appears then it can be easily classified into a well suite category by using K- NN algorithm.
K-NN algorithm can be used for Regression as well as for Classification but mostly it is used for the Classification problems.
K-NN is a non-parametric algorithm, which means it does not make any assumption on underlying data.
It is also called a lazy learner algorithm because it does not learn from the training set immediately instead it stores the dataset and at the time of classification, it performs an action on the dataset.
KNN algorithm at the training phase just stores the dataset and when it gets new data, then it classifies that data into a category that is much similar to the new data.
(iii) Support vector machines (SVMs) :-
Support vector machines are a set of supervised learning methods used for classification, regression and outliers detection.
Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, it is used for Classification problems in Machine Learning.
The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane.
SVM chooses the extreme points/vectors that help in creating the hyperplane. These extreme cases are called as support vectors, and hence algorithm is termed as Support Vector Machine.
(iv) Kernel SVM :-
The kernel functions play a very important role in SVM. Their job is to take data as input and they transform it in any required form. They are significant in SVM as they help in determining various important things.
In this article, we will be looking at various types of kernels. We will also be looking at how the kernel works and why is it necessary to have a kernel function. This will be an important article, as it will give an idea of what kernel function should be used in specific programs.
(v) Naïve Bayes :-
What Does Naïve Bayes Mean? A naïve Bayes classifier is an algorithm that uses Bayes' theorem to classify objects. Naïve Bayes classifiers assume strong, or naïve, independence between attributes of data points. Popular uses of naïve Bayes classifiers include spam filters, text analysis and medical diagnosis. In other words;
Naïve Bayes is a classification algorithm for binary (two-class) and multi-class classification problems. The technique is easiest to understand when described using binary or categorical input values.
(vi) Decision Tree Classification :
Decision Tree is the most powerful and popular tool for classification and prediction. A Decision tree is a flowchart-like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label.
(vii) Random Forest Classification :
The random forest is a classification algorithm consisting of many decisions trees. It uses bagging and feature randomness when building each individual tree to try to create an uncorrelated forest of trees whose prediction by committee is more accurate than that of any individual tree.
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