The aim of supervised, machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. As adaptive algorithms identify patterns in data, a computer “learns” from the observations. When exposed to more observations, the computer improves its predictive performance.

Specifically, a supervised learning algorithm takes a known set of input data and known responses to the data (output), and trains a model to generate reasonable predictions for the response to new data.

For supervised learning to work, it needs a set of input data that is labeled and correct with a well-defined known output.

Supervised learning uses the data patterns to predict the values of additional data for the labels. This method will commonly use in applications where historical data predict likely upcoming event.

## What are the types of Supervised Learning?

Supervised learning splits into two broad categories:

- Classification
- Regression

Regression is the type of Supervised Learning in which labeled data used, and this data is used to make predictions in a continuous form. The output of the input is always ongoing, and the graph is linear. Regression is a form of predictive modeling technique which investigates the relationship between a dependent variable[Outputs] and independent variable[Inputs]. This technique used for forecasting the weather, time series modelling, process optimization.

Classification is the type of Supervised Learning in which labelled data can use, and this data is used to make predictions in a non-continuous form. The output of the information is not always continuous, and the graph is non-linear. In the classification technique, the algorithm learns from the data input given to it and then uses this learning to classify new observation. This data set may merely be bi-class, or it may be multi-class too. Ex:- One of the examples of classification problems is to check whether the email is spam or not spam by train the algorithm for different spam words or emails.

Types of Supervised Learning Algorithms | |

Classification | Regression |

1. Logistic Regression/Classification 2. K-Nearest Neighbors 3. Support Vector Machines 4. Kernel Support Vector Machines 5. Naive Bayes 6. Decision Tree Classification 7. Random Forest Classification | 1. Simple Linear Regression 2. Multiple Linear Regression 3. Polynomial Regression 4. Support Vector Regression 5. Ridge Regression 6. Lasso Regression 7. ElasticNet Regression 8. Bayesian Regression 9. Decision Tree Regression 10. Random Forest Regression |

## Importance of Supervised Learning Algorithms in Machine Learning

Supervised learning algorithms are widely used in machine learning, where there is significant amount of labeled data or historic data. It’s easier to implement and can be specific to few set of accurate predictions.

It required comparatively lesser training and fewer resources. The complexity of the software becomes less.

It’s widely used in fraud detection, image identification, face recognition, image to text and so on.

How Supervised Learning Algorithms are implemented in python.

Python uses scikit learn library which brings all these algorithms easy to use, with few lines of codes. Few of the examples are cited below.

## The Mathematics Behind Supervised Algorithms

The complex mathematics behind each type of supervised algorithms will be provided with notes on each algorithms.