Exam DP-100 topic 3 question 62 discussion

DRAG DROP -
You are creating a machine learning model that can predict the species of a penguin from its measurements. You have a file that contains measurements for three species of penguin in comma-delimited format.
The model must be optimized for area under the received operating characteristic curve performance metric, averaged for each class.
You need to use the Automated Machine Learning user interface in Azure Machine Learning studio to run an experiment and find the best performing model.
Which five actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Select and Place:

Suggested Answer:
Step 1:Create and select a new dataset by uploading he command-delimited file of penguin data.
Step 2: Select the Classification task type
Step 3: Set the Primary metric configuration setting to Accuracy.
The available metrics you can select is determined by the task type you choose.
Primary metrics for classification scenarios:
Post thresholded metrics, like accuracy, average_precision_score_weighted, norm_macro_recall, and precision_score_weighted may not optimize as well for datasets which are very small, have very large class skew (class imbalance), or when the expected metric value is very close to 0.0 or 1.0. In those cases,
AUC_weighted can be a better choice for the primary metric.
Step 4: Configure the automated machine learning run by selecting the experiment name, target column, and compute target
Step 5: Run the automated machine learning experiment and review the results.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-train