No-Code Machine Learning Using Amazon AWS SageMaker Canvas - Building and Using the Model for Prediction - Project 2 - S

The video tutorial guides viewers through the process of creating an SMS spam detection model using SageMaker Canvas. It covers naming the model, selecting the appropriate data set, and building the model by choosing a target column. The tutorial explains how SageMaker Canvas predicts the model type and provides a preview of the model's accuracy. It also discusses the creation of the model, the time it takes, and the advanced metrics available, such as F1 score, precision, and recall. The video concludes with a brief mention of the next steps for using the model to predict test data.

What is the first step in creating a new SMS spam detection model?

• Evaluating the model

• Naming the model

• Selecting the model type

• Choosing a target column

Which tool automatically predicts the model type for the SMS spam detection model?

• TensorFlow

• Keras

• PyTorch

• SageMaker Canvas

What is the estimated accuracy of the model after the initial build?

• 90%

• 85%

• 99%

• 95%

How long does it usually take to create the model with a small dataset?

• 30 to 60 minutes

• 15 to 30 minutes

• 2 to 15 minutes

• 2 to 5 minutes

Which of the following metrics is NOT mentioned for evaluating the model?

• ROC curve

• Recall

• Precision

• F1 score