Demo 2.1 Classification Model

The video tutorial covers the process of training a classification model using machine learning. It begins with an introduction to classification models and prerequisites, followed by setting up the environment in a portal. The tutorial then guides through creating and configuring a dataset, normalizing and splitting data, and training the model. It explains evaluation metrics like accuracy, precision, and recall, and concludes with deploying the model for real-time inference.

How do you calculate accuracy in a classification model?

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What does the AUC value indicate about a model's performance?

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What is the purpose of the ROC curve in model evaluation?

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Describe the steps taken to deploy the classification model for real-time inference.

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