Question

A data scientist wants to launch an AutoML classification experiment directly from a notebook using the Python API rather than the AutoML UI. They have a Spark DataFrame named `churn_df`, the target column is `churned`, and they want AutoML to stop after 30 minutes. Which code snippet correctly starts the experiment?

Accepted Answer

from databricks import automl
summary = automl.classify(dataset=churn_df, target_col="churned", timeout_minutes=30)

Answer

from databricks import automl
summary = automl.fit(dataset=churn_df, label="churned", problem_type="classification", max_time=30)

Answer

import mlflow
summary = mlflow.automl.classify(data=churn_df, target="churned", timeout=30)

Answer

from databricks import automl
summary = automl.run(df=churn_df, target_col="churned", task="classify", minutes=30)