Track your Data Science
skore is a Python library to
evaluate and get insights from your predictive models.
We structure and store your experiments so that you can easily retrieve them
later.
Reports for your Experiments
Create structured reports to quickly evaluate and inspect
your predictive models by using
EstimatorReport
or
CrossValidationReport
and even compare them by using
ComparisonReport.
Given some data
df: (expand for full code)
from skrub.datasets import fetch_employee_salaries
dataset = fetch_employee_salaries()
df = dataset.X
y = dataset.y
import sklearn
import skore
import skrub
model = skrub.tabular_pipeline(sklearn.linear_model.Ridge())
report_ridge = skore.evaluate(model, df, y, splitter=5)
report_ridge.help()
Get Insights that Matter
Quickly generate beautiful visualizations with
display.plot()
error = report_ridge.metrics.prediction_error()
error.plot(kind="actual_vs_predicted")
display.frame()
report_ridge.metrics.summarize().frame(
aggregate=None
)
Store and Retrieve your Reports, Locally or on Skore Hub
Store your reports by using
Project.
Retrieve the most important reports to revisit your insights or compare with
new experiments later.
project = skore.Project(name="adult_census_survey")
project.put("ridge", report_ridge)
When using
Project
with mode="hub", you can view your reports on
Skore Hub.
For seeing an example of project and reports stored on Skore Hub, view the example
here.
Our Community
The skore project is driven by Probabl together with a world-wide
community of contributors. Here we display a randomly selected group of 30
contributors.
Try it yourself!
Ready to write less code, focus on what matters, and store your experiments to
retrieve them later? Dive into skore now and be part of our growing
community!