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Scikit-learn flavor and evaluation

In this exercise you will train a classification model and evaluates its performance. The model uses your Insurance Charges dataset in order to classify if the charges were for a female or male.

We will start by logging our model to MLflow Tracking using the scikit-learn flavor and finish by evaluating your model using an eval_data dataset.

Your evaluation dataset is created as eval_data and our model trained with the name lr_class. The eval_data will consist of X_test and y_test as the training data was split using train_test_split() function from sklearn.

# Model
lr_class = LogisticRegression()
lr_class.fit(X_train, y_train)

The mlflow module is imported.

This exercise is part of the course

Introduction to MLflow

View Course

Exercise instructions

  • Log the lr_class model using scikit-learn "built-in" flavor.
  • Call the evaluate() function from mlflow module.
  • Evaluate the eval_data dataset and target the "sex" column.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Eval Data
eval_data = X_test
eval_data["sex"] = y_test
# Log the lr_class model using Scikit-Learn Flavor
___.___.___(____, "model")

# Get run id
run = mlflow.last_active_run()
run_id = run.info.run_id

# Evaluate the logged model with eval_data data
___.___(f"runs:/{run_id}/model", 
        ____=____, 
        ____="____",
        model_type="classifier"
)
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