Question 1

The boosting process in the AutoML process achieves error reduction by observing the error records in a model and then oversampling misclassified records in the next model created.

Accepted Answer

Boosting in AutoML focuses on sequentially improving the model by specifically targeting the errors of previous models. It increases the weight of misclassified records so that subsequent models pay more attention to them, aiming to reduce these errors over time.

Question 2

Summarize practitioner Elpida Ormanidou's views on the challenges of using AutoML to solve business problems.

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Question 3

The purpose of building models in the AutoML process is to
A) standardize data for a common format.
B) extract insights from data.
C) clean erroneous data.
D) reduce noise and bias.

Accepted Answer

Building models in the AutoML process primarily aims to extract insights from data, enabling users to make informed decisions based on the patterns and predictions generated by these models.

Question 4

Identify a true statement about AutoML.
A) AutoML is mainly an unsupervised approach that employs the traditional, manual approach of standard machine learning.
B) AutoML facilitates accurate decision making for users with limited coding and modeling experience.
C) AutoML, if implemented precisely, can replace human analytical expertise.
D) AutoML capabilities cannot be easily extended to populations beyond traditional data scientists, particularly in medium- and smaller-sized businesses.

Accepted Answer

AutoML (Automated Machine Learning) is designed to automate the process of applying machine learning to real-world problems. It simplifies the model selection, preprocessing, and parameter tuning steps, making it easier for individuals without extensive coding or machine learning background to build and deploy models, thus facilitating more accurate decision-making for users with limited experience.

Question 5

How did Pelephone, one of the oldest and largest mobile phone providers in Israel, increase revenues by using AutoML?

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Question 6

Describe how URBN, a portfolio of apparel brands, employed AutoML to improve overall customer experience.

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Question 7

Describe how Sumitomo Mitsui Card Company (SMCC) in Japan applies AutoML for risk modeling and related marketing applications.

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Question 8

How did Blue Health Intelligence (BHI) benefit through the use of AutoML?

Accepted Answer

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Question 9

Explain the advanced ensemble methods of bagging and boosting.

Accepted Answer

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Question 10

According to a recent survey conducted by Accenture, 40 percent of companies have adopted machine learning for improved sales and marketing performance.

Accepted Answer

Accenture has conducted various surveys and research studies that highlight the adoption of machine learning and artificial intelligence technologies by companies to enhance different aspects of their business, including sales and marketing performance. The specific percentage might vary across different reports and time frames, but it's well-documented that a significant portion of companies are leveraging these technologies for business improvements.

Question 11

In the model recommendation step of the AutoML process, original data outliers and patterns are highlighted.

Accepted Answer

In the model recommendation step of AutoML, the focus is on selecting the best performing models based on the data; it does not specifically involve highlighting outliers and patterns in the original data.

Question 12

The step of creating ensemble models in the AutoML process allows us to
A) handle missing data, outliers, variable selection, data transformation, and data standardization to maintain a common format.
B) provide an understanding of invisible relationships and patterns.
C) extract insight from data.
D) reduce the generalization error of the prediction.

Accepted Answer

Ensemble models combine multiple machine learning models to improve the overall performance, specifically aiming to reduce the generalization error, rather than focusing on data preprocessing or insight extraction.

Question 13

Which of the four key steps in the AutoML process involves handling missing data, outliers, variable selection, data standardization, and data transformation to maintain a common format?
A) preparing data
B) building models
C) recommending models
D) creating ensemble models

Accepted Answer

Preparing data involves handling missing data, outliers, variable selection, data standardization, and data transformation to ensure the data is in a suitable format for model building.

Question 14

List and explain several examples of AutoML applications in marketing.

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Question 15

What is AutoML? Is it better than the traditional, manual approach of standard machine learning?

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Question 16

An ensemble model blends the most favorable elements from all models into a single model.

Accepted Answer

An ensemble model combines the predictions from multiple machine learning models to improve the overall performance, leveraging the strengths of each individual model.

Question 17

Describe how United Airlines employed AutoML to improve overall customer experience.

Accepted Answer

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Question 18

Identify the correct sequence of the four key steps in the AutoML process.
A) recommending models, preparing data, building models, creating ensemble models
B) building models, creating ensemble models, recommending models, preparing data
C) creating ensemble models, preparing data, recommending models, building models
D) preparing data, building models, creating ensemble models, recommending models

Accepted Answer

The correct sequence for the AutoML process starts with preparing data, followed by building models based on that data. After models are built, creating ensemble models helps in improving prediction accuracy by combining multiple models. Finally, recommending models involves selecting the best-performing model(s) for deployment.

Question 19

Appropriate data preparation to ensure the quality of data is an elemental first step in producing accurate model predictions.

Accepted Answer

Data preparation involves cleaning, transforming, and organizing data to improve its quality, which is crucial for producing accurate and reliable model predictions.

Question 20

Explain how some companies are using AutoML tools embedded within their technology platforms to enhance business performance.

Accepted Answer

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Quiz 7: Automated Machine Learning