README.md

MLflow evaluation Examples

The examples in this directory illustrate how you can use the mlflow.evaluate API to evaluate a PyFunc model on the specified dataset using builtin default evaluator, and log resulting metrics & artifacts to MLflow Tracking.

• Example evaluate_on_binary_classifier.py evaluates an xgboost XGBClassifier model on dataset loaded by shap.datasets.adult.
• Example evaluate_on_multiclass_classifier.py evaluates a scikit-learn LogisticRegression model on dataset generated by sklearn.datasets.make_classification.
• Example evaluate_on_regressor.py evaluate as scikit-learn LinearRegression model on dataset loaded by sklearn.datasets.fetch_california_housing

Prerequisites

pip install scikit-learn xgboost shap matplotlib

How to run the examples

Run in this directory with Python.

python evaluate_on_binary_classifier.py
python evaluate_on_multiclass_classifier.py
python evaluate_on_regressor.py