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Classification with multiple labels and probabilities
# mkdocs_gallery_thumbnail_path = 'images/example_thumnail.png'
from krisi import library, score
from krisi.utils.data import generate_random_classification
y, preds, probs, sample_weight = generate_random_classification(
num_labels=5, num_samples=1000
)
score(
y=y,
predictions=preds,
probabilities=probs,
# dataset_type="classification_multilabel", # if automatic inference of dataset type fails
calculation="single",
default_metrics=library.ClassificationRegistry().multiclass_classification_metrics,
).print()
Total running time of the script: ( 0 minutes 0.000 seconds)
Download Python source code: evaluate_classification_multilabel.py
Download Jupyter notebook: evaluate_classification_multilabel.ipynb