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Advanced Visual Interpretation

🎯 Purpose

This guide expands upon standard exploratory data visualizations by focusing on advanced, high-dimensional, and context-specific visual tools. These visuals are designed to support deeper discovery, pattern recognition, and structure validation across complex datasets.

📈 1. Advanced Distribution Analysis

Used for:

  • Identifying subtle non-normality
  • Flagging asymmetric or long-tail features
  • Pre-transform checks
  • QQ Plot (for normality)
from scipy import stats
stats.probplot(df['feature'], dist="norm", plot=plt)
  • Box-Cox Distribution Comparison
  • Histogram with log scale overlay

✔️ Use when assessing need for transformation or normalization

📦 2. Multivariate Outlier Detection

Used for:

  • Catching high-dimensional anomalies
  • Finding data points that don’t fit the core structure
  • Mahalanobis Distance vs Observation Plot
  • Isolation Forest Score Distribution
  • t-SNE or UMAP embedding with outlier overlay

✔️ Color outliers in reduced-dim scatter for visual impact

🔄 3. Feature Interaction Visualization

Used for:

  • Capturing non-linear, conditional, or synergistic relationships
  • Informing new feature creation
  • Interaction Plot by Group
  • LOESS curve overlays on scatterplots
  • PairGrid with color by target or cluster

✔️ Use to justify interaction terms in models

🧬 4. Conditional Distribution by Target or Cluster

Used for:

  • Exploring feature shifts between subgroups
  • Validating groupwise trends
  • Boxen plots split by target or label
  • Density plots with hue = class or group
  • Violin plots for long-tail variables

✔️ Helps flag high-leverage group-specific features

🔍 5. Dimensionality Reduction Projections

Used for:

  • Understanding structure in complex data
  • Pre-clustering and anomaly discovery
  • PCA scatter with color by class or metric
  • UMAP/t-SNE embedding (cluster or group-labeled)
  • Explained variance bar plot

✔️ Use consistent scaling and coloring across runs

🧮 6. Redundancy and Multicollinearity Checks

Used for:

  • Identifying feature duplication or instability
  • Supporting feature pruning decisions
  • Correlation Matrix Heatmap
  • VIF Score Bar Chart
  • Condition Index Plot

✔️ Filter collinear features visually before modeling

🧰 7. Missingness Pattern Maps

Used for:

  • Evaluating missing data structure
  • Detecting systematic vs random gaps
  • Missingno Heatmap / Matrix
  • Custom bar chart of % missing per feature
  • Time series missing block map (if applicable)

✔️ Use to guide imputation method selection

📋 Analyst Visual EDA Checklist

  • [ ] Target-level conditional p