Note
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Clustering with SAVEΒΆ
Sliced Average Variance Estimation is able to find three distinct clusters in a dataset used to classify counterfeit swiss banknotes.
import matplotlib.pyplot as plt
from sliced.datasets import load_banknote
from sliced import SlicedAverageVarianceEstimation
X, y = load_banknote()
save = SlicedAverageVarianceEstimation(n_directions=2, n_slices=2)
X_save = save.fit_transform(X, y)
plt.scatter(X_save[:, 0], X_save[:, 1], c=y, alpha=0.8, edgecolor='k')
plt.xlabel("$X\hat{\\beta}_{1}$")
plt.ylabel("$X\hat{\\beta}_{2}$")
plt.title("Swiss Banknote Data")
plt.show()
Total running time of the script: ( 0 minutes 0.029 seconds)