Experiment tracking for scikit-learn. 🧩 Log, organize, visualize and compare model metrics, parameters, dataset versions, and more.
APACHE-2.0 License
Experiment tracking for scikit-learn–trained models.
# On the command line:
pip install neptune-sklearn
# In Python, prepare a fitted estimator
parameters = {
"n_estimators": 70, "max_depth": 7, "min_samples_split": 3
}
estimator = ...
estimator.fit(X_train, y_train)
# Import Neptune and start a run
import neptune
run = neptune.init_run(
project="common/sklearn-integration",
api_token=neptune.ANONYMOUS_API_TOKEN,
)
# Log parameters and scores
run["parameters"] = parameters
y_pred = estimator.predict(X_test)
run["scores/max_error"] = max_error(y_test, y_pred)
run["scores/mean_absolute_error"] = mean_absolute_error(y_test, y_pred)
run["scores/r2_score"] = r2_score(y_test, y_pred)
# Stop the run
run.stop()
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