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Exploratory data analysis (EDA)

Last updated Sep 9, 2022 Edit Source

How do you “look” at features and high-dimensional examples?

  1. Summary statistics
    • Categorical Features
      • Frequencies
      • Mode
      • Quantiles
    • Numerical Features
      • Location
        • Mean
        • Median
        • Quantiles
      • Spread
        • Range
        • Variance
        • Interquartile ranges
    • Entropy: measured “randomness” of a set of variables where entropy is $- \Sigma_{c=1}^k p_c \log p_c$ and $p_c$ is the proportion of times you have value $c$, range from $[0, \log k]$
      • Low entropy means it is very predictable whereas high entropy means it is very unpredictable (roughly, spread)
      • Normal distribution has the highest entropy
    • Not always representative! Don’t mistake the map for the territory
  2. Distance or similarities
    • Hamming distance: number of times elements aren’t equal
    • Euclidian distance: how far apart are the vectors (square root of sum of squares)
    • Correlation
    • Jaccard coefficient: set distance, intersection over union
    • Edit distance: for strings, how many characters do I need to change to go from one to the other
    • Distance in latent space
  3. Visualizations
    • Basic line plots
    • Matrix plot: visualize two features as an image
    • Correlation plot
      • Can add colour to show a third feature (usually categorical)
    • Scatterplot