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.
Which three features do you think are likely the greatest predictors for a car's price?
Make_model, year, miles.
A car's make/model, year, and miles are likely to be among the strongest predictors for its price.
Color, height, make_model.
A car's height and color are not strong predictors for a car's price.
Miles, gearbox, make_model.
The gearbox isn't a main predictor of price.
Tire_size, wheel_base, year.
Tire size and wheel base aren't strong predictors for a car's price.
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:
If you wanted to understand the types of users that visit the site, would you use supervised or unsupervised learning?
Unsupervised learning.
Because we want the model to cluster groups of related customers,
we'd use unsupervised learning. After the model clustered the users,
we'd create our own names for each cluster, for example,
"discount seekers," "deal hunters," "surfers," "loyal,"
and "wanderers."
Supervised learning because I'm trying to predict which class
a user belongs to.
In supervised learning, the dataset must contain the label you're
trying to predict. In the dataset, there is no label that refers to a
category of user.
Suppose you had an energy usage dataset for homes with the following columns:
What type of ML would you use to predict the kilowatt hours used per
year for a newly constructed house?
Supervised learning.
Supervised learning trains on labeled examples. In this dataset
"kilowatt hours used per year” would be the label because this is the
value you want the model to predict. The features would be
"square footage,” "location,” and "year built.”
Unsupervised learning.
Unsupervised learning uses unlabeled examples. In this example,
"kilowatt hours used per year” would be the label because this is the
value you want the model to predict.
Suppose you had a flight dataset with the following columns:
If you wanted to predict the cost of a coach ticket, would you use
regression or classification?
Regression
A regression model's output is a numeric value.
Classification
A classification model's output is a discrete value,
normally a word. In this case, the cost of a coach ticket is
a numeric value.
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.
It's possible to create a classification model from the dataset.
You would do something like the following:
- Find the average cost of a ticket from the departure airport to
the destination airport.
- Determine the thresholds that would constitute "high," "average,"
and "low".
- Compare the predicted cost to the thresholds and output the
category the value falls within.
No. It's not possible to create a classification model. The
coach_ticket_cost values are numeric not categorical.
With a little bit of work, you could create a classification model.
No. Classification models only predict two categories, like
spam or not_spam. This model would need to predict
three categories.
Classification models can predict multiple categories. They're
called multiclass classification models.
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, but use only the features you believe have the strongest predictive power for the label.
Retraining the model with fewer features, but that have more predictive power, can produce a model that makes better predictions.
You can't fix a model whose predictions are far off.
It's possible to fix a model whose predictions are off. Most models require multiple rounds of training until they make useful predictions.
Retrain the model using a larger and more diverse dataset.
Models trained on datasets with more examples and a wider range of values can produce better predictions because the model has a better generalized solution for the relationship between the features and the label.
Try a different training approach. For example, if you used a supervised approach, try an unsupervised approach.
A different training approach would not produce better predictions.
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.