Purpose of Diagnostic Plots

Diagnostic plots are visual tools designed to evaluate the validity of assumptions made by a statistical or machine-learning model. These assumptions include linearity, normality of residuals, homoscedasticity, and the absence of influential points. In R Programming Language Diagnostic plots help analysts and data scientists identify potential problems with the model, guiding them in making informed decisions about model improvement or transformation.

Model evaluation is a critical step in the lifecycle of any statistical or machine-learning model. Diagnostic plots play a crucial role in assessing the performance, assumptions, and potential issues of a model. In this comprehensive overview, we will delve into the theory behind diagnostic plots, their types, and their interpretation.