.png){kind=icon}
.png){kind=icon}
Return
Explainable Linear and Generalized Linear Models by the Predictions Plot
.png){kind=icon}
PRE
AI
DOI：10.1080/00031305.2025.2539235.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Multiple linear regression is a basic statistical tool, yielding a prediction formula with the input variables, slopes, and an intercept. But is it really easy to see which terms have the largest effect, or to explain why the prediction of a specific case is unusually high or low? To assist with this the so-called predictions plot is proposed. Its simplicity makes it easy to interpret, and it combines much information. Its main benefit is that it helps explainability of the prediction formula as it is, without depending on how the formula was derived. The input variables can be numerical or categorical. Interaction terms are also handled, and the model can be linear or generalized linear. Another display is proposed to visualize correlations and covariances between prediction terms, in a way that is tailored for this setting.
Keywords:
Effect size
Explainability
Linear prediction
Regression
Visualization