Top Five Regression Algorithms

According to the recent study, it has been found that machine learning algorithms have almost replaced 25% of the jobs around the world and are expected to replace more in the next 10 years. A Machine Learning algorithm which is considered to be a set of definite procedures is mainly used for building a production ready machine learning model for our data. As we know, there are mainly two types of Machine Learning Algorithms namely Supervised Machine Learning Algorithm and Unsupervised Machine Learning Algorithm.

So, in this particular blog, we will deep dive into the Top five Regression Algorithms which fall under the family of Supervised Machine Learning algorithms. By formal definition, we can say that Regression algorithms predict the output values based on input features and build a model depending on the features of training data and finally predict the value for new data with the help of the model.

Logistic Regression Algorithm:

One of the most commonly used regression techniques used for classification problems is Logistic Regression. It is the most beneficial model in the industry sectors as it can be extensively applied across credit card fraud detection and scoring or maybe in clinical trials. Some of the major advantages include that it is easier to implement and interpret, one can include more than one dependent variable which can be dichotomous, and it provides a quantified value to measure the strength of association consistent with the rest of variables. Despite its popularity, it has some drawbacks too such as citing a lack of robust technique and also a great model dependency. Moreover, Logistic Regression should not be considered in cases when the number of observations is lesser than the number of features as it may lead to overfitting.

Application: 

  • Suppose we want to predict if snowfall will happen tomorrow in Gangtok. Here the outcome of the prediction is not a continuous number  but one of the several categories and this is where logistic regression comes into play.
  • Nowadays, enterprises deploy Logistic Regression to predict house prices in land business, or customer lifetime value within the insurance sector which are leveraged to produce a continuous outcome such as whether a customer can buy it or not.

Are you a programming lover? If yes, then check out the latest article on how to Implement Logistic Regression in R.

Linear Regression Algorithm:

Linear Regression model is a statistical method that shows the relationship between two variables and how the change in one variable impacts the other one. It assumes a linear relationship between the input variables (x) and therefore the output variable (y). When we refer to a single input variable (x), the method is called a simple linear regression whereas when there are multiple input variables, the procedure is referred to as multiple linear regression.

The principal advantage of linear regression includes its simplicity, linearity, interpretability, widespread availability and scientific acceptance. Linear regression assumes a linearity between the variables but the data is rarely linearly separable and so the assumption turns out to be wrong many times. This regression is commonly used in financial portfolio prediction, salary forecasting, real estate predictions and in traffic when arriving at ETAs.

Application:

  • Linear Regression is extensively used in business sectors, or for sales forecasting based on the trends. When a company observes steady increase in sales every month, then the linear regression comes as it helps the company forecast in sales in upcoming months.
  • On the other hand, Linear Regression also helps assess risk involved in insurance or financial domain. This analysis helps insurance companies find that older customers tend to form more insurance claims. Such analysis results play an important role in business decisions and are made to account for risk.

Lasso Regression Regression Algorithm:

LASSO which stands for Least Absolute Selection Shrinkage Operator uses shrinkage wherein shrinkage means a constraint on parameters. The goal of lasso regression is to get the subset of predictors that helps to minimize prediction error for a quantitative response variable. The algorithm operates by imposing a constraint on the model parameters which cause the regression coefficients for a few variables to shrink toward a zero.  It is quite advantageous as it provides a good prediction accuracy, because shrinking and removing the coefficients reduce variance without a substantial increase of the bias. Lasso regression algorithms are widely utilized in financial networks and economics.

Application: 

  • In finance, the algorithm is applied in forecasting probabilities of default. Moreover, Lasso-based forecasting models help in assessing enterprise-wide risk framework. 
  • Lasso-type regressions are also used to perform stress test platforms to research on multiple stress scenarios.  

Support Vector Machines Regression Algorithm:

Support Vector Machine (SVM) is another most powerful algorithm with strong theoretical foundations and works relatively well when there is a transparent margin of separation between classes. This supervised machine learning algorithm has strong regularization, is more simpler and effective in high dimensional spaces, memory efficient and may be leveraged both for classification or regression challenges. On the other hand, SVM is not suitable for giant datasets or when the dataset contains more noise. 

There are two classes of SVM namely Linear SVM(linearly separable data) and Non-linear SVM (non-linearly separated data).

Application: 

  • Support vector machines regression algorithms have found several applications within the oil and gas industry, classification of images, text and hypertext categorization.
  • SVM can also be used for identifying the classification of genes, and other biological problems too.
  • In the oilfields, it is specifically leveraged for exploration to know the position of layers of rocks and creation of 2D and 3D models as a representation of the subsoil.

Multivariate Regression Algorithm:

This technique is employed when there is more than one predictor variable in a multivariate regression model. It helps us to understand among the variables present in our dataset. Considered to be one of the simplest supervised machine learning algorithms by data analysts, this regression algorithm predicts the response variable for a set of explanatory variables.  These regression techniques are often implemented efficiently with the assistance of matrix operations  The main advantage is that the conclusion drawn after the implementation of this algorithm is more accurate, realistic and nearer to real life situations as compared to other algorithms.

Application:

  • Industry application of Multivariate Regression algorithm is seen heavily within the retail sector where customers make a choice on a variety of variables like brand, price and merchandise .
  • By building a Multivariate regression model, an agriculture scientist can predict his total crop yield for the next summer and collect details of the expected soil conditions and rainfall. He can also try to understand the relationship among the variables.

The multivariate algorithm helps decision makers to seek out the simplest combination of things to extend footfalls within the store.

Author
Senior Data Scientist and Alumnus of IIM- C (Indian Institute of Management - Kolkata) with over 25 years of professional experience Specialized in Data Science, Artificial Intelligence, and Machine Learning. PMP Certified, ITIL Expert certified APMG, PEOPLECERT and EXIN Accredited Trainer for all modules of ITIL till Expert Trained over 3000+ professionals across the globe currently authoring a book on ITIL "ITIL MADE EASY".