Cumulative Explained Variance for PCA Component Selection

A line plot showing the cumulative proportion of explained variance as a function of the number of Principal Component Analysis (PCA) components. This visualization helps determine the optimal number of components to retain by displaying the trade-off between dimensionality reduction and information preservation. The cumulative curve typically exhibits an elbow pattern where additional components yield diminishing returns, and horizontal threshold lines (e.g., 90%, 95%) guide component selection decisions.

Implementations

• Cumulative Explained Variance for PCA Component Selection in Makie.jl (Julia)
• Cumulative Explained Variance for PCA Component Selection in Altair (Python)
• Cumulative Explained Variance for PCA Component Selection in Bokeh (Python)
• Cumulative Explained Variance for PCA Component Selection in lets-plot (Python)
• Cumulative Explained Variance for PCA Component Selection in Matplotlib (Python)
• Cumulative Explained Variance for PCA Component Selection in Plotly (Python)
• Cumulative Explained Variance for PCA Component Selection in plotnine (Python)
• Cumulative Explained Variance for PCA Component Selection in Pygal (Python)
• Cumulative Explained Variance for PCA Component Selection in Seaborn (Python)
• Cumulative Explained Variance for PCA Component Selection in ggplot2 (R)