No wonder that Machine Learning has become the hottest trend in the technological and analytical hub and is continuously breaking the obstacles in its passageways.
However, it would only possible because Machine Learning consists of amazing tools and techniques that boots up ML in the market and give strength to hold up brilliant applications in various domains.
Moving to another learning in terms of ML techniques, today we will learn various types of regression techniques through this blog. There are multitudinous types of regression to perform that owing tremendous characteristic and specific conditions where they are best tailored to practice.
Usually, the very first thoughts that come to mind when having words about regression techniques in data science are linear and logistics regressions, even though, people end up their learning with these two popular ML algorithms considering that they are only the two types of regression.
Most widely used regression techniques are employed for investigating or examining the relationship between the dependent and independent set of variables.
It is a broad term covering the variety of data analysis techniques that are used in qualitative-exploratory research for analyzing infinite variables and mainly used for forecasting, time series analysis modelling, and identifying cause-effect relationships.
Indeed, majorly seven types of regression techniques are firmly used for complex problems among all types of regression study.
To establish the possible relationship among different variables, various modes of statistical approaches are implemented, known as regression analysis. In order to understand how the variation in an independent variable can impact the dependent variable, regression analysis is specially moulded out. Basically;
Consider the example, after watching a specific television commercial slot, the exact number of companies can be estimated using data to count maximum effort for that particular slot. The finance and insurance industry depends a lot on regression analysis for data surveys.
Types of regression analysis can be selected on the attributes, target variables, or the shape and nature of the regression curve that exhibit the relationship between dependent and independent variables. Below is the discussion for types of regression techniques;
It is the simplest regression technique used for predictive analysis, a linear approach for featuring 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.
Although, linear regression faces the issue of overfitting, and possess an equation: Y = bX+C, where Y is a dependent variable and X, is the independent variable, that shows a best fitted straight line(regression curve) having b as the slope of the line and C intercept.
Simple Linear Regression
As said earlier linear regression is the simplest regression technique, it is fast and easy to model and useful when the target relationship is not complex or enough data is not available, it is very perceptive for detecting outliers and easy to learn and evaluate.
It is preferred when the dependent variable is binary (dichotomous) in nature, it predicts the parameters of a logistics model and in the form of binomial regression that is widely used to analyze categorical data.
In layman’s words, Logistic Regression is preferred when to ascertain the probability of an event in terms of either success or failure, if the dependent variable is binary( 0 or 1), true or false, yes or no, logistics regression is used.
The relationship between the dependent and independent variables are calculated by computing probabilities using the logit function.
It deals with the data having two certain measures and the connection between the measures and the predictors. It holds the equation: Y=a0+x1a1 +x2a2.
It is implemented for analyzing numerous regression data. When multicollinearity occurs, least-square calculations get unbiased, then a bias degree is affixed to the regression calculations that yield a reduction in standard errors through ridge regression.
In simple words, sometimes the regression model becomes too complex and approaches to overfit, so it is worthwhile to minimize the variance in the model and save it of overfitting. So ridge regression corrects the size of the coefficients.
Ridge regression acts as a remedial measure used to ease collinearity in between predictors of a model, since, the model includes correlated featured variables, so the final model is confined and rigid in its maximum approach.
It is a widely used regression analysis to perform both variable selection and regularization, it adopts easy shielding (thresholding) and picks a subset of the covariates given for the implementation of the final model.
Lasso (Least Absolute Shrinkage Selector Operator) Regression reduces the number of dependent variables, in a similar case of ridge regression, if the penalty term is huge, coefficients can be reduced to zero and make feature selections easier. It is called termed as L1 regularization.
When to execute a model that is fit to manage non-linearly separated data, the polynomial regression technique is used. In it, the best-fitted line is not a straight line, instead, a curve that best-fitted to data points.
It is represented by the equation: Y=b0+b1x1+b2x22+........bn xnn
It is widely deployed for a curvilinear form of data and best fitted for least-squares methods. It focuses on modelling the expected value of the dependent variable (Y) with respect to the independent variable (x).
It is highly used to meet regression models with predictive models that are carried out naturally. With every forward step, the variable gets added or subtracted from a group of descriptive variables.
The criteria followed in the stepwise regression technique are forward determination (forward selection), backward exclusion (backward elimination), and bidirectional removal (bidirectional elimination).
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It is the mixture of ridge and lasso regression that brings out a grouping effect when highly correlated predictors approach to be in or out in the model combinedly. It is recommended to use when the number of predictors is very greater than the number of observations.
It is a traditional regression technique that linearly combined the fines of lasso and ridge regression methods, and used in SVM (Support Vector Machine Algorithm), metric training, and document optimizations.
(Related reading: 7 Major Branches of Discrete Mathematics)
1. Multicollinearity - When the independent variables are highly correlated to each other, then variables are said to possess multicollinearity.
It is assumed in many regression techniques that multicollinearity doesn’t exist in the dataset, as it makes task complex in selecting the important featured variables.
2. Outliers - In every dataset, there must be some data points that have low or high value as compared to other data points, i.e those data points don’t relate to the population termed as outliers, an extreme value.
3. Heteroscedasticity - When the variation in the dependent variables is not even crosswise the values of independent variables, it is described as heteroscedasticity.
For instance, when there is variation in the income of two persons, then the variability in food consumption also occurs.
There you have it, we have discussed the 7 most common types of regression analysis that are important in Data Science and Machine Learning (ML). In the nutshell, regression analysis is a set of statistical techniques and methods that enables one to formulate a predicted mathematical equation between the creative effects and performance outcomes and shows the casual-effect connection.
Moreover, the selection of picking the right regression technique entirely depends on the data and requirements needed to apply. I expect you have savoured studying this blog and surely receive something new.
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