Classification Types

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 Classification Types 

In machine learning, classification is supervised learning in which the machine learns from data, and by using this trained data it classifies the new data.
-We have a few types of classification algorithms in machine learning:

Linear classifiers

● Logistic regression

● Naive Bayes classifier

Non-Linear classifiers

● K- nearest neighbor

● Support vector machines

● Decision trees

● Random forest

● Neural networks 

Logistic regression(Predictive learning model)

Logistic Regression is a mathematical model used in statistics to predict the probability of an event using the trained data. The predicted output always lies between 0 and 1.
The goal of logistic regression is to find the best fitting model to describe the relationship between the dependent and independent variables.
Naive Bayes Classifiers(Generative learning model)

The Naive Bayes model is mostly used for large datasets. This is a method of assigning class labels with help of acyclic directed graphs. This graph has one parent node and multiple child nodes. Each child node is assumed to be independent and separate from the parent.

K- Nearest Neighbor

The k-nearest neighbor’s algorithm is a supervised classification technique that takes a bunch of labeled points and uses them to learn how to label the other points(new data). It chooses the nearest point in labeled data to label the new data. Once it checks with all the k number of nearest neighbors, it assigns a label based on which label is the nearest of the neighbors.

Support Vector Machine

A support vector machine is a series of machine learning algorithms used for performing both classification and regression analysis. This model algorithm creates a line called hyperplane which categorizes the data into different classes. This labeled data of each category is trained to the algorithm so that it is able to categorize the new data.
Decision Trees

A decision tree is a flow chart like a model that contains conditional control statements comprising decisions and their probable consequences. Decision trees can be used to solve problems with discrete attributes as well as boolean functions. The topmost node is called the root node which is known as the best predictor. A decision node has two or more branches and a leaf node represents a classification or decision.

Neural Networks

A neural network consists of units arranged in layers, which converts an input vector into an output. Each unit takes an input, applies the function to it, and passes the output to the next layers. Weightings are applied to the signals passing from one unit to another unit. 


Random forest

Random forest is considered as the ensemble of the decision trees. This algorithm combines all the outputs of the multiple decision trees to generate a single output.







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