Human Activity Recognition VisualizationΒΆ

  • Target distribution
  • F=5.08E+04, F=4.63E+04, F=4.44E+04, F=4.12E+04, F=4.04E+04, F=3.75E+04, F=3.26E+04, F=3.25E+04, F=3.22E+04, F=3.13E+04
  • Top feature interactions, 0.685, 0.684, 0.683, 0.682
  • Discriminating PCA directions, 0.645, 0.630, 0.568, Scree plot (PCA explained variance)
  • Discriminating LDA directions, 0.863, 0.829, 0.806, 0.785
Target looks like classification
/home/circleci/project/~/miniconda/envs/testenv/lib/python3.11/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped_vals = vals.groupby(grouper)
Showing only top 10 of 561 continuous features
/home/circleci/project/dabl/plot/utils.py:607: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  for name, group in data.groupby(target)[column]:
Linear Discriminant Analysis training set score: 0.984

# sphinx_gallery_thumbnail_number = 3
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_openml
from dabl import plot

X, y = fetch_openml('har', as_frame=True, return_X_y=True)

plot(X, y)
plt.show()

Total running time of the script: (0 minutes 17.796 seconds)

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