If we want to train AI to classify fruits, which of these can be used as a “feature”?

• Apple, Banana, Orange

• Sweetness

• Fruit or Vegetable

• Tasty or Not

In machine learning, what does “classification” mean?

• Predicting a number

• Sorting files into folders

• Predicting a category

• Making the graph bigger

When classifying fruits vs vegetables in class activity, which two features were compared?

• Sweetness and easy to eat

• Color and size

• Shape and weight

• Expensive and cheap

If a fruit’s data point is exactly on the dividing line of the graph, what should the model do?

• Ignore the fruit

• The model must still decide

• Put the fruit in both groups

• Ask a human to remove the data

On the fruit/veggie graph, each point represents what?

• The dividing line

• A group of fruits

• A single fruit or vegetable

• The accuracy of the model

Why is a line drawn on the fruit/veggie graph?

• To make the graph look nice

• To separate the data into two groups

• To connect all the points

• To show the biggest fruit

If a fruit’s point is very close to the dividing line, why might the model struggle?

• The fruit is too big

• The data is missing

• The model may have a hard time deciding its category

• The model always ignores close points

In shape recognition, which of these can be a feature for classifying shapes?

• Number of sides

• Color of the pen

• Size of the paper

• Name of the shape

If we include too many features when training a model, what might happen to accuracy?

• It always becomes perfect

• It sometimes becomes less accurate

• It will have no effect

• It makes the model faster

What is the main goal of Lesson 7 in App Lab?

• Learn how to draw shapes

• Learn how to import and use models in an app

• Learn how to code in Python

• Learn how to use spreadsheets