  # What is supervised machine learning and types?

Supervised machine learning is a subset of machine learning and artificial intelligence. Supervised learning is distinguished by the use of labeled datasets to train algorithms that properly categorize data or predict outcomes. In the cross-validation process, the model is trained to recognize the underlying patterns between the input data and the output labels. Supervised learning models may be used to create and enhance a variety of commercial applications such as image and object identification, prescriptive modeling, consumer sentiment, and spam filtering. Here are a few types of supervised machine learning:

## Regression

Regression is a statistical method for determining the connection between dependent and independent variables. It is often used to generate predictions, such as those for a company’s sales revenue. If there is a link between the input variable and the output variable, regression methods are utilized. The following are some  Regression algorithms that fall under the category of supervised machine learning:

## Linear Regression

Linear regression in supervised machine learning is commonly used to predict future events by identifying the connection between a dependent variable and one or more independent variables. When plotted on a graph, this line is straight.

## Regression Trees

Decision trees are built using an algorithmic technique that discovers multiple ways to partition a data set depending on certain parameters. Decision Trees are a non-parametric supervised machine learning approach that may be used for classification as well as regression applications.

## Bayesian Linear Regression

The naive Bayes classification method indicates that the existence of one character has no influence on the presence of another in the likelihood of a particular event, and each predictor has the same effect on that result. This method is most commonly employed in text categorization, spam detection, and recommendation systems.

## Polynomial Regression

Polynomial regression models in supervised machine learning function similarly to multiple linear regression, but with a non-linear curve. These data points are transformed into polynomial characteristics of a particular degree by the model, which is then modeled using a linear model.

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## Classification

When the output variable is categorical, such as Yes-No, True-False, and so on, classification techniques are employed. Linear classifiers are common classification methods, which are discussed in further depth below.

## Random Forest

Random forest is a supervised machine learning technique that uses a number of decision trees on different sets of a given data and averages them to enhance the accuracy rate of that data. The original input sample size is always used as the sub-sample size, but the samples are generated using replacement.

## Decision Trees

Each internal node represents the “test” for an attribute, each branch indicates the outcome of the test, and each leaf node represents the final choice or result. A decision tree generates a set of rules that may be used to categorize data.

## Logistic Regression

Whereas linear regression is used when the dependent variable is continuous, logistical regression is used when the dependent variable is categorical, such as spam detection. Can it only have one of two possible values? In that situation, you might choose to examine your data using logistic regression.

## Support Vector Machines (SVM)

The training data is represented as points in space split into categories in the support vector machine. Then, new instances are mapped into the same area. It creates a hyperplane with the greatest distance between two classes of data points.

## K-nearest neighbor

The neighbor-based classification does not attempt to build a generic internal model; instead, it merely retains instances of the training data. K-nearest neighbor, which classifies data points based on their closeness to other accessible data. KNN is commonly employed in recommendation engines and image recognition.

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