Scree plot
A scree plot graphs explained variance, eigenvalues, or another fit measure against increasing component or cluster count to help select an appropriate count.
Statistics Slam Dunk
A scree plot displays a quantity such as within-cluster variation against the number of clusters to help identify an appropriate cluster count.
- How it is used
- Guides selection of K before fitting a K-means clustering solution.
- How deep it goes
- Explains axes, repeated variance calculations, elbow interpretation, and subjectivity, but defers detailed silhouette computation.
- Examples
- Uses NBA team payrolls and regular-season wins, comparing candidate K values from 2 through 7.
- What you can do after
- Evaluate candidate cluster counts and choose a defensible K for K-means, while checking the elbow against silhouette results.
Data Without Labels
A scree plot is a graph of component eigenvalues or explained variance used to inspect how much information successive components capture.
- How it is used
- Guides principal-component selection by showing how much cumulative variance each additional component retains.
- How deep it goes
- Explains the visualization and applies it to component selection, but does not develop scree-plot theory beyond PCA variance calculations.
- Examples
- Uses standardized Iris measurements, four principal components, a cumulative-variance plot, and a two-component projection.
- What you can do after
- Apply it via scikit-learn, NumPy, and Matplotlib to select a practical number of PCA components.
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