Note
Click here to download the full example code
Classification with Probabilities
# mkdocs_gallery_thumbnail_path = 'images/example_thumnail.png'
from krisi import score
from krisi.utils.data import generate_random_classification
y, preds, probs, sample_weight = generate_random_classification(
num_labels=2, num_samples=1000
)
sc = score(
y=y,
predictions=preds,
probabilities=probs,
# dataset_type="classification_binary_balanced", # if automatic inference of dataset type fails
calculation="single",
)
sc.print()
sc = score(
y=y,
predictions=preds,
probabilities=probs,
# dataset_type="classification_binary_balanced", # if automatic inference of dataset type fails
calculation="both",
)
sc.print()
sc.generate_report()
Total running time of the script: ( 0 minutes 0.000 seconds)
Download Python source code: evaluate_classification_with_probabilities.py
Download Jupyter notebook: evaluate_classification_with_probabilities.ipynb