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.

What is the prerequisite for understanding the classification model demo?

• Understanding clustering models

• Familiarity with regression model demo

• Experience with data visualization

• Knowledge of deep learning

What type of dataset is created for the classification model in the demo?

• Image dataset

• Audio dataset

• Tabular dataset

• Text dataset

Which step is performed after normalizing the data in the classification model pipeline?

• Data visualization

• Data splitting

• Model evaluation

• Feature extraction

Which metric is NOT used for evaluating classification models?

• Accuracy

• Root Mean Square Error

• Recall

• Precision

What does a high AUC value indicate about a classification model?

• The model is not reliable

• The model is performing well

• The model is overfitting

• The model is underfitting

What is the default threshold value used in the classification model?

• 0.7

• 0.3

• 0.5

• 0.1

Which two metrics are commonly used to evaluate classification models?

• Precision and Recall

• Mean Absolute Error and R-squared

• F1 Score and Root Mean Square Error

• Silhouette Score and Davies-Bouldin Index

What is the purpose of deploying the classification model?

• To improve model accuracy

• To reduce data size

• To perform real-time inference

• To visualize the data

Which service is used for deploying the model in a production environment?

• Azure Kubernetes Service

• Azure Functions

• Azure Container Instances

• Azure Blob Storage

What is the role of the Python script module in the inference pipeline?

• To visualize data

• To split data

• To clean data

• To return the predicted column