Supervised Machine Learning: Regression and Classification

specialization in machine learning


Supervised Machine Learning is a type of Machine Learning in which machines are trained with the adequate utilization of labeled training data, and the output is predicted on the basis of that data. The training data provided to the machines function as the supervisor that teaches the machines how to predict the output correctly. 

A majority of practical Machine Learning utilizes supervised learning, and it is where you have access to input variables (x) and an output variable (y), and you make use of an algorithm to learn the mapping function from the input to the output. 


Supervised Learning can be considered a process of providing input data along with correct output data to the specialization in machine learning model. The goal of a supervised learning algorithm is to determine a mapping function in order to map the input variable with the output variable. 


Supervised Learning can be utilized for image classification, spam filtering, risk assessment, and fraud detection. In this type of learning, models are trained with the adequate utilization of labeled datasets. In supervised learning, models are trained to utilize the labeled dataset, where the model grabs an understanding of each type of data. 

Regression and Classification Supervised Learning 

Regression and Classification are the two algorithms of Supervised Learning, and both are utilized for prediction in the field of Machine Learning and work with the labeled datasets. However, the major difference between Regression and Classification algorithms is that Regression Algorithms are utilized to predict continuous values such as salary, price, and age. 


Classification algorithms, on the other hand, are utilized to predict/classify discrete values such as Spam, Not Spam, True or False, or Male or Female. It can be considered a process of finding a function that assists in dividing the dataset into classes based on several parameters. A computer program is trained on the training dataset and is based on that training.


Regression can be considered a process of determining correlations between dependent and independent variables. It assists in the process of prediction of continuous variables such as the prediction of House prices and other market trends. It is when the output variable is a continuous or real value, such as weight or salary. Several models can be utilized, and the simplest of them is linear regression. 

Summing Up

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