Building and evaluating ML models
Graded Quiz • 30 min Quiz10 Questions Week 2
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Building and evaluating ML models

Graded Quiz • 30 min Quiz10 Questions Week 2
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Q:

Input data in a machine learning model is often made of three parts, which are: the features of an example, the resulting label, and the label type. How can the ‘features’ be defined in a machine learning context?

Q:

You work at a medical research facility that analyzes patient data for local hospitals. You want to use machine learning for specialized image recognition in order to identify bacterial infections in patients’ x-ray images. What is the preferred method of obtaining a labeled dataset for this custom image recognition use case?

Q:

You work at a mobile phone manufacturer and are preparing to launch the newest version of your high-end phone. You want to analyze the battery efficiency of your new phone against previous models. You have a backlog of historical data on previous models and their results, but these datasets exist in silos separate from the data for your new phone. How can you acquire a labeled dataset in this scenario when datasets exist in separate silos?

Q:

You work at a car manufacturing company that is ready to deploy a machine learning model. However, you want to evaluate the model first and decide to evaluate your model with a small set of data. You cannot measure how accurate the model is on all the original training data because it could memorize all answers and perform badly after deployment. What is a reasonable percentage of the data to reserve when you are evaluating the accuracy of a machine learning model?

Q:

You are the communications manager at a marketing company. Recently, you noticed an increase in spam marketing emails disguised as popular brand emails that you want to filter out of your inbox. You want to use machine learning to predict which emails are spam and should be filtered. What are some possible features in this machine learning use case to detect deceptive spam emails?

Q:

You lead the marketing team for a startup accommodation booking website. You want to provide users with personalized accommodation recommendations, but lack sufficient historical labeled data of customer bookings to use as an exclusive data source. Instead, you and your team have only been using user clicks and accommodation viewings as a proxy for your entire dataset. What is the issue of only using user clicks and accommodation viewings as your dataset that might lead to few converted bookings?

Q:

You are a doctor at a small medical clinic studying the symptoms and effects of common health conditions. You want to use machine learning to predict which of your patients might have an increased probability of heart disease. However, you have a limited dataset due to having fewer patients than a full-sized hospital. What would be the preferred solution to identify patients with an increased probability of heart disease using machine learning?

Q:

You are working on the data team at a global banking company. You are gathering a wide variety of labeled data from different departments and locations for future machine learning experiments. Before you can introduce the data to train the machine learning model, what do you need to do?

Q:

You work in the customer retention team at a bank and have noticed an increase in customers leaving your service. To solve this problem, you use machine learning with an objective to improve customer retention at your bank by personalizing services and loans. What is the preferred optimization of your objective to improve customer experience and retention at your bank?

Q:

Machine learning projects consist of many different phases. However, a lot of useful information cannot be described in the phases alone, such as guidance on machine learning best practices. What is considered an example of good practice in machine learning?

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