What are different models in machine learning?

The program that trains the learning sequence of a machine is called the learning model of that machine. A machine learning model is a programmed pattern of training that makes conclusions from the dataset that is previously used in machine learning. There are different machine learning models which are divided into different factors, like the type of task given to the machine.

Models in Machine Learning

The process of algorithmic learning methods to find certain scenarios and give outputs is known as the machine learning model. A specific pattern or output is found from the dataset, while training is called the machine learning model with some specified rules. A common example of machine learning is face detection or face recognition. The learning model detects the different faces of the input image and then measures the different alignments involved in the method. Finally, faces are identified from the database.

Classification of Different Models in Machine Learning

The machine learning model is broadly classified into three types viz Supervised learning, Unsupervised learning, and the last one is Reinforcement learning. Let us have a brief look over them one by one.

Supervised Learning Model

Supervised as the name suggests, indicates something that is managed and so supervised learning tries to set a learning algorithm that has some previous labels to forecast upcoming observations. The learning model of a machine where the selected algorithm fetches information from the previously labeled input and uses it for the next output. A large set of data is first labeled and then moved to the algorithm to find the targeted observation. The labeled dataset improved the learning ability of machines it is previously known. It helps in risk management, spam, and fraud detection through frequent accuracy tests.

Supervised learning is further divided into their subtypes as follows −

Classification

A classification model is used where there is categorized algorithm is needed. The supervised learning model is where the response is categorized as a dual decision like, yes or no, vacant or engaged, white or black, red or blue, etc. Is called a classified supervised learning model. It helps mainly in spam detection.

Regression

The regression model is the exact opposite of the classification model. Here, the algorithm is not categorized and is used where the response is not categorized. The regression model is a type of supervised learning where continuous values are given to forecasting new observations, Examples of the regression model are weather forecasting, price ups, and downs, medical diagnosis, voice recognition, etc.

Unsupervised Learning Model

Unsupervised learning clears its definition by its name only. Unsupervised learning contains no labeled dataset or predictions to make the machine learn. The learning model which uses an unlabeled and clustered bunch of datasets is known as an unsupervised learning model. This falls under no supervision of the machine or any entity.

The unsupervised learning model is further classified into the following subtypes −

Clustering

The technique of data fetching in which a group of unlabeled data is present on the basis of their similarities and dissimilarities is called clustering. Clustering processes uncategorized, raw datasets and bundles them in a form of a cluster on the basis of some patterns, similarities, and dissimilarities to fetch the datasets.

Association Rule

In this algorithm, the aim of user is to create some relation or association between the bjects from the large datasets. It creates the relatonship between various variables of a dataset. Using this technique, we can predict the occurance of a particular object on the basis of other associated objects of that dataset.

The K-Means Algoithm

It is a type of unsupervised machine learning algorithm that is used to solve the problems that exist in a group or cluster. In this approach, the data sets are separated into groups or clusters so that all the data types are of different categories. The data points inside a cluster should be homogenous and heterogenous to data points of other clusters.

Dimensity Reduction

There are many variables, features and input present in a dataset and these entities are known as “Dimensity”. This machine learning model is used to reduce these dimensities. The purpose of this learning model is to convert the higher dimensions into lesser dimansions dataset so that the similar kind of data can be separated.

Reinforcement Learning Model

The reinforcement learning model is defined as a feedback-taking model, in which the target of the model is to get maximum positive feedback for a particular system. In it, various software and machines work together to find the best and optimal solution for a particular problem. In this training model, the next action taken by the model is decided on the basis of the output of the previous result. There are two types of feedback that a system needs to perform viz Positive and Negative feedback. Let us have a brief look over these two types of feedback below.

Positive

Positive reinforcement is defined as the event in which an action gets positive feedback from the system. Giving positive feedback for a particular action increases the courage of the machine to do such kind of action again. So that the maximum reward can be obtained by the training model.

Negative

Negative reinforcement is defined as the event in which an action gets negative feedback from the system. Giving negative feedback for a particular action decreases the courage of the machine to avoid such kind of action again. So that the minimum negative feedback can be obtained by the training model.

Conclusion

• The process of algorithmic learning methods to find certain scenarios and give outputs is known as the machine learning model.

• The most important and common machine learning algorithm known is Convolutional Neural Network (CNN). CNN works on the principle of extracting inputs from images.

• The machine learning model is broadly classified into three types that are Supervised learning, Unsupervised learning, and Reinforcement learning.

• The various types of Supervised learning are Classification and Regression.

• The various types of Unsupervised learning are Clustering, Visualization, Projection, and Density Estimation.

• The reinforcement learning model is defined as a feedback-taking model, in which the target of the model is to get maximum positive feedback against a particular problem.

• The two types of feedback that we get in Reinforcement learning are Positive feedback and Negative feedback.