Data Science Model Deployments and Cloud Computing on GCP - Lab - Add Model Evaluation Step in Kubeflow before Deploymen

The video tutorial covers the process of model evaluation in a credit card fraud detection project using a Jupyter notebook. It explains the use of Pandas and scikit-learn for model evaluation, focusing on the random forest classifier. The tutorial details the function 'threshold_check' for accuracy validation and the DSL pipeline for condition checking. It also includes a practical implementation of the pipeline, highlighting the importance of deleting previous endpoints to avoid unnecessary costs.

Explain the significance of the Roc curve in model evaluation.

What condition is checked before executing the code block in the DSL PIPELINE?

What mistake did the teacher rectify regarding the folder name?