Principle:DistrictDataLabs Yellowbrick Scatter Feature Visualization

Knowledge Sources	Exploratory Data Analysis
Domains	Feature_Analysis, Visualization
Last Updated	2026-02-08 05:00 GMT

Overview

Technique for visualizing the relationship between two features as a scatter plot with class-based point coloring to reveal separability and distribution patterns.

Description

Bivariate scatter visualization plots each observation as a point in 2D feature space, colored by its target class label. This reveals inter-class separability, feature correlations, outliers, and distribution shapes. It is one of the most fundamental exploratory data analysis techniques and supports feature selection by identifying which feature pairs best separate classes.

Usage

Use this principle during exploratory data analysis when assessing feature quality and class separability in bivariate feature space.

Theoretical Basis

For features $x_1,x_2$ and class labels $y$:

$\text{Plot: }(x_{1i},x_{2i})\text{ colored by }{y}_i\mathrm{\forall }i\in \{1,...,n\}$

Good class separability is indicated by minimal overlap between class-colored clusters.

Related Pages

• Implementation:DistrictDataLabs_Yellowbrick_ScatterVisualizer