Note
Click here to download the full example code
Default Metric Selection
# mkdocs_gallery_thumbnail_path = 'images/example_thumnail.png'
import numpy as np
from krisi import library, score
score(
y=np.random.random(1000),
predictions=np.random.random(1000),
default_metrics=library.RegressionRegistry().all_regression_metrics, # This is the default
).print()
score(
y=np.random.random(1000),
predictions=np.random.random(1000),
default_metrics=library.RegressionRegistry().minimal_regression_metrics,
).print(input_analysis=False)
score(
y=np.random.random(1000),
predictions=np.random.random(1000),
default_metrics=library.RegressionRegistry().low_computation_regression_metrics,
).print(input_analysis=False)
Total running time of the script: ( 0 minutes 0.000 seconds)
Download Python source code: evaluate_default_metric_selection.py
Download Jupyter notebook: evaluate_default_metric_selection.ipynb