Regression analysis is an integral concept of Machine Learning. Regression is used to investigate the relationship between independent features/variables and dependent outcome/variables. In Machine Learning, this is used as a way for predictive modelling since regression is one of the key elements in various applications.
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Regression analysis can benefit businesses and organisations by providing crucial insight for decision making. It is used to forecast share or stock prices, houses, and real estate prices and map salary changes.
Let’s discuss what regression analysis is and how it is helpful for businesses and organisations in this article.
What is regression? Understanding Machine Learning regression
Regression is an understanding of the connection between independent features/variables and a dependent outcome/variables. It helps estimate the relationship between dependent and independent variables and predict their outcomes. People who study statistics know regression is an integral part of the subject. Moreover, regression is a crucial part of forecast models in Machine Learning.
It is an excellent approach for predicting continuous outcomes in predictive modelling, making it a widely used model. In Machine Learning regression, the concept plots a line of the best fit through various data points. To achieve the best fit line, you have to minimise the distance between an individual point and the line.
Along with classification, regression is a primary application of a supervised type of Machine Learning. Classification is categorising objects depending on their learned features. On the other hand, regression is forecasting continuous outcomes. Both these are predictive modelling issues. Supervised Machine Learning is integral in both cases as an approach as regression and classification models rely greatly on labelled output and input training data. The labelling is easy and convenient for the model to understand the relationship.
What is regression analysis?
Regression analysis is a statistical method for understanding the relationship between different dependent variables/outcomes and independent variables. Models trained for forecasting outcomes and trends are given training using various regression techniques. These models learn the relationship between input and output data from labelled training data. This is important for understanding gaps in historical data.
As applicable to all supervised Machine Learning, it is vital to ensure labelled training data represents the whole population. If the training data is not representative, the predictive model will overfit data that doesn’t represent unseen and new data, leading to inaccurate predictions. Regression analysis includes relationships between outcomes and features. Therefore the proper selection of features is significant.
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Some important terms to know about regression analysis
Let’s learn some essential regression analysis terms in-depth to gather more information on the topic. Some of them are as follows:
- Independent variable – Independent variable is also known as a predictor. Independent variables are factors that impact the dependent variables or are used to predict the values of dependent variables.
- Dependent variable – The dependent variable is also called the target variable. The dependent variable is the main factor in regression analysis.
- Outliers – Outlier is an observation. It either contains a very high value or a low value compared to other observed values. Outliers tend to hamper results, and thus, they are best avoided.
- Multicollinearity – Multicollinearity happens when independent variables are correlated to one another than other variables. It shouldn’t be there in the dataset. If present, it has a high chance of creating problems.
- Overfitting & Underfitting– Overfitting is a problem when the algorithm works well with the training dataset but not with the test dataset. When the algorithm does not perform even with the training dataset, it is known as underfitting.
What is the purpose of using regression analysis?
Regression analysis helps in predicting a continuous variable. In the real world, predictions can be beneficial. For instance, getting predictions on weather conditions helps you plan a trip, and accurate future predictions in marketing trends help achieve profiting business decisions. Regression analysis is a reliable statistical method extensively used in Data Science and Machine Learning.
Mentioned below are some reasons for using regression analysis:
- For finding trends in data
- Estimates the relationship between the independent and the target variable
- Predicts continuous/real values
- Determines the least important factor, the most critical factor, and its impact
Different types of regression in Machine Learning
Machine Learning and Data Science have different kinds of regression, with each carrying its separate importance and use. However, all regression techniques analyse the impact of independent variables on dependent variables. Among the many kinds, linear regression needs special mention.
What is linear regression?
In the linear regression method, you plot a straight line within data points to minimise errors between the line and the data points. The relationship between the dependent and the independent variables is assumed to be linear in this case. However, one small problem with the linear regression technique is outliers due to the best fit straight line.
We will use a simple diagram and chart to explain linear regression.
In this image, we represent and predict an employee’s salary in an organisation depending on the years of experience.
There is a mathematical equation for Linear Regression:
Y = aX + b
Here Y = dependent variables (target variables)
X = independent variables (predictor variables)
a and b are linear coefficients
If there is a single input variation (x), this linear regression is called simple linear regression. Linear regression with more than one input variable is known as multiple linear regression.
Some popular applications of linear regression
Linear regression finds its usage in many places. Some of the applications of regression include:
- Predictions in the real estate market
- Analysing sales estimates and marketing trends
- Salary predictions and forecasting
- Arriving at ETAs in traffic
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With the implementation of Data Science and Machine Learning, productivity and performance in an organisation will boost. Regression analysis professionals can perform a regression analysis for better forecasts and predictions. Decision-making in a business becomes easy with implementation.
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Is regression a kind of Machine Learning?
Regression is considered a supervised machine learning technique used for predicting and forecasting continuous values.
What are the different kinds of regression analysis techniques?
There are different regression analysis techniques, and various factors guide and impact these techniques. The various categories are: 1. Linear Regression, 2. Ridge Regression, 3. Logistic Regression, 4. Polynomial Regression, 5. Lasso Regression. Bayesian Linear Regression
How do you calculate linear regression?
The equation for linear regression is Y = a + bX, Y is the dependent variable - it goes on the Y-axis, X is the independent variable - it is plotted on X-axis, a is the Y-intercept, and b is the slope of the line