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rep_missing's Introduction

rep_missing

Dataset (Pure Numerical):

Classification:

  1. banknote https://archive.ics.uci.edu/dataset/267/banknote+authentication (1372, 4)

  2. yeast https://archive.ics.uci.edu/dataset/110/yeast (1484, 8)

  3. climate_model_crashes https://archive.ics.uci.edu/dataset/252/climate+model+simulation+crashes (540, 18)

Regression:

  1. wine_quality_white https://archive.ics.uci.edu/dataset/186/wine+quality (4898, 11)

  2. yacht_hydrodynamics https://archive.ics.uci.edu/dataset/243/yacht+hydrodynamics (308, 6)

  3. concrete_compression https://archive.ics.uci.edu/dataset/165/concrete+compressive+strength (1030, 8)

Dataset (Pure Category)

  1. Breast Cancer Classification https://archive.ics.uci.edu/dataset/14/breast+cancer (286, 9)

  2. Solar Fire Regression https://archive.ics.uci.edu/dataset/89/solar+flare
    (1389, 12)

  3. Car Evaluation https://archive.ics.uci.edu/dataset/19/car+evaluation (1728, 6)

Preprocessing:

  1. No Normalization Required
  2. Missing Type: MCAR, MAR(Optimal Transport), MNAR(Optimal Transport - Logistic)
  3. Missing Rate: 5%, 10%, 30%, 50%.
  4. Missing Column: All columns

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