# Machine Learning Regression 101: A Beginner’s Guide

Ever wondered how scientists can predict things like the weather, or how economists know when the stock markets will rise or dip? Well, machine learning regression is a magical tool behind all of these forecasts.

Regression is one of the most important and broadly used machine learning and statistics tools. It allows a user to make predictions out of raw data by understating the relationship between variables. Machine Learning Regression is used all around us, and in this article, we are going to learn about machine learning tools, types of regression, and the need to ace regression for a successful machine learning career.

Source: Iberdrola

**What is Machine Learning Regression?**

Regression is a machine learning method that allows a user to predict a continuous outcome variable (y) based on the value of one or multiple predictor variables (x). The outcome is a mathematical equation that defines y as a function of the x variables.

Amongst the various kinds of machine learning regression, linear regression is one of the simplest & most popular for predicting a continuous variable. As the name suggests, it assumes a linear relationship between the outcome and the predictor variables. For instance, a machine learning regression is used for predicting prices of a house, given the features of the house like size, price, etc.

Let’s have a look at some types of regressions used in machine learning.

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**Types of Regression **

**1. Simple Linear Regression**

The most basic regression model, linear regression, fits a line to data points on an x-y axis. Used mostly for predictive analysis, this technique features the relationship between the response and predictors or descriptive variables. It mainly considers the conditional probability distribution of the response presents the predictor’s uses.

Y = mX + c

- Y – dependent variable
- X – independent variable
- m – slope of the line
- c – intercept

A very important machine learning tool, the regression technique is very perceptive for detecting outliers and easy to learn and evaluate.

Example – Prediction of sales of umbrella basis rainfall happening that season.

Source: Javapoint

**2. Logistic Regression**

This machine learning regression technique is used when the dependent variable is discrete – 0 or 1, true or false, etc. Suggestively, this means that the dependent variable has only two values. It is represented by a sigmoid curve showcasing the relationship between the target variable and the independent variable. The function behind logistic regression is the Logit function- i.e. the relationship between the dependent and independent variables are calculated by computing probabilities using the logit function.

logit(p) = ln(p/(1-p)) = b0+b1X1+b2X2+b3X3….+bkXk

- p – probability of occurrence of the feature

Example – Logistic regression is mainly used for classification problems. For instance, classifying whether an email is a spam or not spam.

**3. Polynomial Regression**

Your dataset might not always be linear, and the variables might not always be categorical in nature. Polynomial regression comes into play when you want to execute a model that is fit to manage non-linearly separated data. In polynomial regression, the best-fitted line is not a straight line, instead, a curve that fits into a majority of data points.

Y=b0+b1x1+b2x22+........bn xnn

- Y – dependent variable
- X – independent variable

This machine learning regression technique is different from others since the power of independent variables is more than 1.

Example – Prediction of sales of umbrella basis rainfall happening that season, when the data is not linearly correlated.

Source: Javapoint

**Machine Learning Tools**

To implement these various types of regressions in machine learning, one needs to be familiar with the different machine learning tools & systems. With the help of ML systems, we can examine data, learn from it and make informed decisions. Let’s look at some popular ones below:

Tools |
Platform |
Language |
Algorithms |

Scikit Learn | Linux, Mac OS, Windows | Python, Cython, C, C++ | Classification, Regression, Clustering, etc. |

Weka | Linux, Mac OS, Windows | Java | Data preparation, Classification, Regression, Clustering, etc. |

Apache Mahout | Cross-platform | JavaScala | Preprocessors, Regression, Clustering, etc. |

Accors.Net | Cross-platform | C# | Classification, Regression, Distribution, Clustering, etc. |

Shogun | WindowsLinux, UNIXMac OS | C++ | Regression, Classification, Clustering, etc. |

Data Scientists usually use platforms like Python & R to run various types of regressions, but other platforms like Java, Scala, C# & C++ could also be used.

**Machine Learning Career**

A career in data science and machine learning can be very rewarding, especially if you start early. With the volume of information being collected by companies all across the world, there is surely a dearth of people who can infer observations using techniques like regression. You have already taken the first step by learning the 101 of machine learning regression, all you need now is take a mentoring approach to learn AI/ ML in detail and prepare hard for that Machine Learning interview.

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