Machine Learning MCQ: Machine Learning Multiple Choice Questions

What is lazy learning?

(Mostly) whenever we see kernel visualizations online (or some other reference) we are actually seeing:

In statistics, what is defined as the probability of a hypothesis test of finding an effect - if there is an effect to be found?

Your data science team is working on a machine learning product that can act as an artificial opponent in video games. The team is using a machine learning algorithm that focuses on rewards: If the machine does some things well, then it improves the quality of the outcome. How would you describe this type of machine learning algorithm?

How is machine learning related to artificial intelligence?

In K-nearest neighbor, the closer you are to neighbor, the more likely you are to

You created machine learning system that interacts with its environment and responds to errors and rewards. What type of machine learning system is it?

You want to create a machine learning algorithm to identify food recipes on the web. To do this, you create an algorithm that looks at different conditional probabilities. So if the post includes the word flour, it has a slightly stronger probability of being a recipe. If it contains both flour and sugar, it even more likely a recipe. What type of algorithm are you using?

You work for an insurance company. Which machine learning project would add the most value for the company!

Which statement about K-means clustering is true?

Random forest is modified and improved version of which earlier technique?

You work for an organization that sells a spam filtering service to large companies. Your organization wants to transition its product to use machine learning. It currently a list Of 250,00 keywords. If a message contains more than few of these keywords, then it is identified as spam. What would be one advantage of transitioning to machine learning?

The error function most suited for gradient descent using logistic regression is

You work in a data science team that wants to improve the accuracy of its K-nearest neighbor result by running on top of a naive Bayes result. What is this an example of?

You and your data science team have 1 TB of example data. What do you typically do with that data?

You want to identify global weather patterns that may have been affected by climate change. To do so, you want to use machine learning algorithms to find patterns that would otherwise be imperceptible to a human meteorologist. What is the place to start?

You want to create a supervised machine learning system that identifies pictures of kittens on social media. To do this, you have collected more than 100,000 images of kittens. What is this collection of images called?

You work for a large credit card processing company that wants to create targeted promotions for its customers. The data science team created a machine learning system that groups together customers who made similar purchases, and divides those customers based on customer loyalty. How would you describe this machine learning approach?

Your data science team wants to use ml to better filter out spam messages. The team has gathered a database of 100,000 messages that have been identified as spam or not spam. If you are using supervised ml, what would you call this data set?

Your organization allows people to create online professional profiles. A key feature is the ability to create clusters of people who are professionally connected to one another. What type of machine learning method is used to create these clusters?

You work for a website that helps match people up for lunch dates. The website boasts that it uses more than 500 predictors to find customers the perfect date, but many costumers complain that they get very few matches. What is a likely problem with your model?

The security company you work for is thinking about adding machine learning algorithms to their computer network threat detection appliance. What is one advantage of using machine learning?

What is Q-learning reinforcement learning?

\_\_\_\_ looks at the relationship between predictors and your outcome.

Why is it important for machine learning algorithms to have access to high-quality data?

Your data science team wants to use the K-nearest neighbor classification algorithm. Someone on your team wants to use a K of 25. What are the challenges of this approach?

 With traditional programming, the programmer typically inputs commands. With machine learning, the programmer inputs

Your data science team must build a binary classifier, and the number one criterion is the fastest possible scoring at deployment. It may even be deployed in real time. Which technique will produce a model that will likely be fastest for the deployment team use to new cases?

You are using K-nearest neighbor and you have a K of 1. What are you likely to see when you train the model?

Someone of your data science team recommends that you use decision trees, naive Bayes and K-nearest neighbor, all at the same time, on the same training data, and then average the results. What is this an example of?

You work for a website that enables customers see all images of themselves on the internet by uploading one self-photo. Your data model uses 5 characteristics to match people to their foto: color, eye, gender, eyeglasses and facial hair. Your customers have been complaining that get tens of thousands of fotos without them. What is the problem?

The new dataset you have just scraped seems to exhibit lots of missing values. What action will help you minimizing that problem?

Which choice is the best example of labeled data?

The model will be trained with data in one single batch is known as ?

Compared to the variance of the Maximum Likelihood Estimate (MLE), the variance of the Maximum A Posteriori (MAP) estimate is ____

You need to quickly label thousands of images to train a model. What should you do?

Your machine learning system is attempting to describe a hidden structure from unlabeled data. How would you describe this machine learning method?

Your university wants to use machine learning algorithms to help sort through incoming student applications. An administrator asks if the admissions decisions might be biased against any particular group, such as women. What would be the best answer?

What is one reason not to use the same data for both your training set and your testing set?

Which loss function would fit best in a categorical (discrete) supervised learning ?

Which of the following methods can use either as an unsupervised learning or as a dimensionality reduction technique?

What is the main motivation for using activation functions in ANN?

Why is naive Bayes called naive?

What is stacking?

The supervisor asks to create a ml system that will help your hr dep. classify job applicants into well-defined groups.What type of system are more likely to recommend?

Asian user complains that your company's facial recognition model does not properly identify their facial expressions. What should you do?

**___** refers to a model that can neither model the training data nor generalize to new data.

Many of the advances in machine learning have come from improved?

You are working on a project that involves clustering together images of different dogs. You take image and identify it as your centroid image. What type machine learning algorithm are you using?

In the HBO show Silicon Valley, one of the characters creates a mobile application called Not Hot Dog. It works by having the user take a photograph of food with their mobile device. Then the app says whether the food is a hot dog. To create the app, the software developer uploaded hundreds of thousands of pictures of hot dogs. How would you describe this type of machine learning?

You create a decision tree to show whether someone decides to go to the beach. There are three factors in this decision: rainy, overcast, and sunny. What are these three factors called?

Your company wants to predict whether existing automotive insurance customers are more likely to buy homeowners insurance. It created a model to better predict the best customers contact about homeowners insurance, and the model had a low variance but high bias. What does that say about the data model?

In 2015, Google created a machine learning system that could beat a human in the game of Go. This extremely complex game is thought to have more gameplay possibilities than there are atoms of the universe. The first version of the system won by observing hundreds of thousands of hours of human gameplay; the second version learned how to play by getting rewards while playing against itself. How would you describe this transition to different machine learning approaches?

Which choice is best for binary classification?

You are part of data science team that is working for a national fast-food chain. You create a simple report that shows trend: Customers who visit the store more often and buy smaller meals spend more than customers who visit less frequently and buy larger meals. What is the most likely diagram that your team created?

You work for a power company that owns hundreds of thousands of electric meters. These meters are connected to the internet and transmit energy usage data in real-time. Your supervisor asks you to direct project to use machine learning to analyze this usage data. Why are machine learning algorithms ideal in this scenario?

You work for a large pharmaceutical company whose data science team wants to use unsupervised learning machine algorithms to help discover new drugs. What is an advantage to this approach?

How do machine learning algorithms make more precise predictions?

What is an example of a commercial application for a machine learning system?

Your supervisor asks you to create a machine learning system that will help your human resources department classify jobs applicants into well defined groups. What type of system are you more likely to recommend?

To predict a quantity value. use \_\_\_\_.

You need to select a machine learning process to run a distributed neural network on a mobile application. Which would you choose?

Are data model bias and variance a challenge with unsupervised learning?

You work for a music streaming service and want to use supervised machine learning to classify music into different genres. Your service has collected thousands of songs in each genre, and you used this as your training data. Now you pull out a small random subset of all the songs in your service. What is this subset called?

Suppose we would like to perform clustering on spatial data such as the geometrical locations of houses. We wish to produce clusters of many different sizes and shapes. Which of the following methods is the most appropriate? ?

What does it mean to underfit your data model?

In traditional computer programming, you input commands. What do you input with machine learning?

Self-organizing maps are specialized neural network for which type of machine learning?

Your company wants you to build an internal email text prediction model to speed up the time that employees spend writing emails. What should you do?

Which of the following is NOT supervised learning? ?