Test Your Understanding

The following questions help you solidify your understanding of core ML concepts.

Predictive power

Supervised ML models are trained using datasets with labeled examples. The model learns how to predict the label from the features. However, not every feature in a dataset has predictive power. In some instances, only a few features act as predictors of the label. In the dataset below, use price as the label and the remaining columns as the features.

An labeled example of automobile attributes.

Which three features do you think are likely the greatest predictors for a car's price?
Tire_size, wheel_base, year.
Make_model, year, miles.
Color, height, make_model.
Miles, gearbox, make_model.

Supervised and unsupervised learning

Based on the problem, you'll use either a supervised or unsupervised approach. For example, if you know beforehand the value or category you want to predict, you'd use supervised learning. However, if you wanted to learn if your dataset contains any segmentations or groupings of related examples, you'd use unsupervised learning.

Suppose you had a dataset of users for an online shopping website, and it contained the following columns:

An image of an row of customer attributes.

If you wanted to understand the types of users that visit the site, would you use supervised or unsupervised learning?
Unsupervised learning.
Supervised learning because I'm trying to predict which class a user belongs to.

Suppose you had an energy usage dataset for homes with the following columns:

An image of a row of home attributes.

What type of ML would you use to predict the kilowatt hours used per year for a newly constructed house?
Supervised learning.
Unsupervised learning.

Suppose you had a flight dataset with the following columns:

An image of a row of flight data.

If you wanted to predict the cost of a coach ticket, would you use regression or classification?
Classification
Regression
Based on the dataset, could you train a classification model to classify the cost of a coach ticket as "high," "average," or "low"?
Yes, but we'd first need to convert the numeric values in the coach_ticket_cost column to categorical values.
No. Classification models only predict two categories, like spam or not_spam. This model would need to predict three categories.
No. It's not possible to create a classification model. The coach_ticket_cost values are numeric not categorical.

Training and evaluating

After we've trained a model, we evaluate it by using a dataset with labeled examples and compare the model's predicted value to the label's actual value.

Select the two best answers for the question.

If the model's predictions are far off, what might you do to make them better?
Retrain the model using a larger and more diverse dataset.
Try a different training approach. For example, if you used a supervised approach, try an unsupervised approach.
Retrain the model, but use only the features you believe have the strongest predictive power for the label.
You can't fix a model whose predictions are far off.

You're now ready to take the next step in your ML journey:

  • People + AI Guidebook. If you're looking for a set of methods, best practices and examples presented by Googlers, industry experts, and academic research for using ML.

  • Problem Framing. If you're looking for a field-tested approach for creating ML models and avoiding common pitfalls along the way.

  • Machine Learning Crash Course. If you're ready for an in-depth and hands-on approach to learning more about ML.