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Regression

Steps

  1. Overview
  2. Pre-requisites
  3. Key Choices and Guidelines
  4. Implementation

1 Overview​

Description​

Regression is one of the most widely used predictive modelling techniques, applied to predict values of continuous numerical attributes or figures, like a price, a cost value, etc. Various regression models for different use cases and different strengths and popularity have evolved. Linear regression, for example, helps to uncover linear relationships, i.e., straight-line relationships, between input (independent) and output (dependent) numerical variables, making it a popular technique for predictive and statistical modeling. Tree-based algorithm implementations like Random Forest or Gradient Boosting Regression have become trending implementations, and proved to create robust models.

Expected Outcome​

The expected outcome is a model that predicts the value of a dependent variable based on one or more independent variables. In the case of linear regression, this model is represented by a linear equation that minimizes the difference between predicted and actual values (ground truth).