AWS Certified Data Analytics Specialty 2021 - Hands-On! - [Exercise] Amazon Machine Learning - Part 2

The video tutorial covers the process of training and evaluating a regression model using Amazon Machine Learning. It explains the difference between numerical regression and classification problems, and discusses the evaluation results of a model compared to a baseline. The tutorial highlights the importance of data cleaning, especially in handling outliers, and demonstrates real-time predictions. It concludes with practical insights into using Amazon ML for predictive modeling.

What is the main challenge in predicting quantities based on past behavior?

• Limited data availability

• Wide range of quantities ordered

• Inaccurate data collection

• Complexity of algorithms

What type of problem is it when predicting a number with meaningful order?

• Clustering

• Numerical regression

• Multiclass classification

• Binary classification

Which classification problem involves choosing between more than two categories?

• Hierarchical classification

• Multiclass classification

• Numerical regression

• Binary classification

What was the model's quality score compared to the baseline?

• Significantly better

• Slightly better

• Worse

• Equal

What is a key step in preparing data for machine learning algorithms?

• Increasing data size

• Using complex algorithms

• Reducing data dimensions

• Cleaning data of outliers

Why might some buyers purchase large quantities of items?

• Due to discounts

• For gifting purposes

• To resell or for institutional needs

• For personal use

What can improve the performance of a machine learning model?

• Reducing the dataset size

• Cleaning the data

• Increasing the number of features

• Using more complex algorithms