Wine Classification Dataset VisualizationΒΆ

  • Target distribution
  • F=2.34E+02, F=2.08E+02, F=1.90E+02, F=1.35E+02, F=1.21E+02, F=1.01E+02, F=9.37E+01, F=3.69E+01, F=3.58E+01, F=3.03E+01, F=2.76E+01, F=1.33E+01, F=1.24E+01
  • Top feature interactions, 0.968, 0.950, 0.946, 0.902
  • Discriminating PCA directions, 0.950, 0.905, 0.868, Scree plot (PCA explained variance)
  • Discriminating LDA directions, 1.000
Target looks like classification
Linear Discriminant Analysis training set score: 1.000

# sphinx_gallery_thumbnail_number = 4
import matplotlib.pyplot as plt
from sklearn.datasets import load_wine
from dabl import plot
from dabl.utils import data_df_from_bunch

wine_bunch = load_wine()
wine_df = data_df_from_bunch(wine_bunch)

plot(wine_df, target_col='target')
plt.show()

Total running time of the script: ( 0 minutes 3.429 seconds)

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