7 Regression Techniques you should know!
Introduction
Regression analysis is a type of predictive modelling technique that looks at how an independent variable (s) and a dependent variable (target) are related (predictor). This method is used to discover the causal relationship between the variables and for forecasting, time series modelling, and forecasting. Regression is the ideal method for studying, for instance, the relationship between rash driving and the number of accidents a driver causes on the road.
For data analysis and modelling, regression analysis is a crucial technique. Here, we attempt to minimize the discrepancies between the data points' varying distances from the curve or line by fitting a curve or line to them.
Regression algorithms and techniques are one of the first algorithm new learners choose to learn but they often forget that there are multiple techniques that can be used to perform regression analysis. Regression techniques can not only helpful in performing linear regression tasks but also are used in classification tasks.
In this blog, we will see 7 such regression techniques that one should know?
Table of contents:
2. Logistic regression
3. Polynomial regression
4 Stepwise regression
5. Ridge regression
6. Lasso regression
7. ElasticNet regression
8. Conclusion