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WorksheetsMachine Learning Overview
Total questions: 34
Worksheet time: 18mins
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?
multiclass classification diagram
linear regression and scatter plots
pivot table
K-means cluster diagram
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 product would look for new patterns in spam messages.
The product could go through the keyword list much more quickly.
The product could have a much longer keyword list.
The product could find spam messages using far fewer keywords.
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?
data cluster
Supervised set
big data
test data
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?
It was consistently wrong.
It was inconsistently wrong.
It was consistently right.
It was equally right end wrong.
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?
Find labeled data of sunny days so that the machine will learn to identify bad weather.
Use unsupervised learning have the machine look for anomalies in a massive weather database.
Create a training set of unusual patterns and ask the machine learning algorithms to classify them.
Create a training set of normal weather and have the machine look for similar patterns.
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?
regression
boosting
bagging
stacking
What is an example of a commercial application for a machine learning system?
a data entry system
a data warehouse system
a massive data repository
a product recommendation system
What does this image illustrate?
a decision tree
reinforcement learning
K-nearest neighbor
a clear trendline
a stopword list words are to be
(a)
noise vs outlier
outiler = anomaly
noise = mislabeled example
noise should be ignored
outlier should be ignored
Why is naive Bayes called naive?
It naively assumes that you will have no data.
It does not even try to create accurate predictions.
It naively assumes that the predictors are independent from one another.
It naively assumes that all the predictors depend on one another.
You work for an ice cream shop and created the chart below, which shows the relationship between the outside temperature and ice cream sales. What is the best description of this chart?
It is a linear regression chart.
It is a supervised trendline chart.
It is a decision tree.
It is a clustering trend chart.
How is machine learning related to artificial intelligence?
Artificial intelligence focuses on classification, while machine learning is about clustering data.
Machine learning is a type of artificial intelligence that relies on learning through data.
Artificial intelligence is form of unsupervised machine learning.
Machine learning and artificial intelligence are the same thing.
How do machine learning algorithms make more precise predictions?
The algorithms are typically run more powerful servers.
The algorithms are better at seeing patterns in the data.
Machine learning servers can host larger databases.
The algorithms can run on unstructured data.
You work for an insurance company. Which machine learning project would add the most value for the company!
Create an artificial neural network that would host the company directory.
Use machine learning to better predict risk.
Create an algorithm that consolidates all of your Excel spreadsheets into one data lake.
Use machine learning and big data to research salary requirements.
What is one reason not to use the same data for both your training set and your testing set?
You will almost certainly underfit the model.
You will pick the wrong algorithm.
You might not have enough data for both.
You will almost certainly overfit the model.
Should you be aware of bias in ML models?
Machine learning algorithms are based on math and statistics, and so by definition will be unbiased.
There is no way to identify bias in the data.
Machine learning algorithms are powerful enough to eliminate bias from the data.
All human-created data is biased, and data scientists need to account for that.
What is stacking?
The predictions of one model become the inputs another.
You use different versions of machine learning algorithms.
You use several machine learning algorithms to boost your results.
You stack your training set and testing set together.
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?
centroid reinforcement
K-nearest neighbor
binary classification
K-means clustering
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?
unsupervised machine learning
binary classification
supervised machine learning
reinforcement learning
What is this diagram a good example of?
K-nearest neighbor
a decision tree
a linear regression
a K-means cluster
Random forest is modified and improved version of which earlier technique?
aggregated trees
boosted trees
bagged trees
stacked trees
Self-organizing maps are specialized neural network for which type of machine learning?
semi-supervised learning
supervised learning
reinforcement learning
unsupervised learning
Which statement about K-means clustering is true?
In K-means clustering, the initial centroids are sometimes randomly selected.
K-means clustering is often used in supervised machine learning.
The number of clusters are always randomly selected.
To be accurate, you want your centroids outside of the cluster.
You created machine learning system that interacts with its environment and responds to errors and rewards. What type of machine learning system is it?
supervised learning
semi-supervised learning
reinforcement learning
unsupervised learning
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?
random forest
logistic regression
KNN
deep neural network
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?
supervised learning
unsupervised learning
reinforcement learning
semi-unsupervised learning
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?
It uses unsupervised learning to cluster together transactions and supervised learning to classify the customers.
It uses only unsupervised machine learning.
It uses supervised learning to create clusters and unsupervised learning for classification.
It uses reinforcement learning to classify the customers.
You are using K-nearest neighbor and you have a K of 1. What are you likely to see when you train the model?
high variance and low bias
low variance and high bias
high bias and high variance
low bias and low variance
Are data model bias and variance a challenge with unsupervised learning?
No, data model bias and variance are only a challenge with reinforcement learning.
Yes, data model bias is a challenge when the machine creates clusters.
Yes, data model variance trains the unsupervised machine learning algorithm.
No, data model bias and variance involve supervised learning.
Which choice is best for binary classification?
K-means
Logistic regression
Linear regression
Principal Component Analysis (PCA)
With traditional programming, the programmer typically inputs commands. With machine learning, the programmer inputs
supervised learning
data
unsupervised learning
algorithms
Why is it important for machine learning algorithms to have access to high-quality data?
It will take too long for programmers to scrub poor data.
If the data is high quality, the algorithms will be easier to develop.
Low-quality data requires much more processing power than high-quality data.
If the data is low quality, you will get inaccurate results.
In K-nearest neighbor, the closer you are to neighbor, the more likely you are to
share common characteristics
be part of the root node
have a Euclidean connection
be part of the same cluster
