Regression Algorithms

Introduction

Regression is a widely used statistical and machine learning tool that is crucial for predictive analytics. Projecting continuous numeric results from given inputs is the key objective of regression projects. What the model produces is influenced by its learning phase. Regression models use input data features and corresponding continuous numeric output values to identify the relationship between these values.

There are two varieties of regression models:

• Unidimensional regression model: In this simplest regression model, the predictions derive solely from a single attribute of the data.
• Multidimensional regression model: This model formulates predictions based on several data attributes.

Different Regression Algorithms

Simple linear regression is a statistical method employed for analyzing and summarizing relationships between two continuous variables. Under the linear regression model, an assumed linear association exists between input variables (x) and a single output variable (y). A linear combination of these input variables can be used to calculate y. Single input variable (x) situations utilize a simple linear regression method. This method transforms to multiple linear regression when there are multiple input variables.

Logistic regression is a commonly used regression method in sectors such as credit scoring and clinical trial studies. The versatility of this popular method to include several dependent variables, either continuous or dichotomous, makes it an attractive choice. One major advantage of this supervised machine learning method is its ability to provide a quantitative reading of the strength of associations related to other variables. Nevertheless, its shortcomings as emphasized by experts include a lack of robustness and high dependency on the model.

Another prominent and robust algorithm is the Support Vector Machine (SVM), which is in the hierarchy of supervised machine learning techniques due to a high degree of regularization. SVMs solve classification and regression problems effectively. The algorithm stands out due to its use of kernels, sparse solutions, and capacity control achieved via the regulation of parameters like the margin and the number of support vectors, among others. The SVM method applies z-score normalization on numeric features as it performs native operations on them. SVM techniques employ epsilon-insensitive loss function to address the regression challenges.

LASSO regression aims to identify a subset of predictors that minimize prediction error for a quantitative reactive variable. The method constrains model parameters, forcing some variable regression coefficients to decline to zero. The residual variable has the closest relationship with non-zero regression coefficient variables following the shrinking process. The predictors in LASSO regression can be numeric, categorical, or a blend of the two.

Conclusion

Regression, a data mining functionality, predicts numeric values on a continuous scale. Various characteristics like profit, revenue, mortgage rates, residential prices, geographic area, climate, and distance can be forecasted using regression algorithms. A regression model could for instance predict the value of a house based on factors such as its location, number of rooms, plot size, and other parameters.

The initial step in a regression task involves accumulating data with known target values. A regression model predicting house prices could be developed based on data of multiple houses over time. Apart from the value, the data could incorporate the houses' age, square footage, number of rooms, proximity to retail areas, school district, taxes, and more. The house value would be the target, the other parameters would serve as predictors, and the data for each house would stand as a record.