Machine Learning Concepts

Choose the word that matches the definition. Finding patterns in data that doesn’t have any labels.

• Features

• Unsupervised Learning

• Supervised Learning

• Training

Choose the word that matches the definition. The process of giving examples to a model so it can learn.

• Features

• Model

• Supervised Learning

• Training

True or False: Machines can learn patterns we didn't intend. For instance, if all the red fish in our training set have a triangular mouth we may generate a model that predicts fish color based on mouth shape.

• True

• False

True or False: Bias in training sets can have serious unintended consequences, such as people not receiving needed medical care or being denied employment.

• True

• False

What is the output you are trying to decide or predict with a model?

• training

• Feature

• Machine Learning Model

• Label

What is the process of giving examples to a model so it can learn?

• Training

• Feature

• Machine Learning Model

• Label

What are the inputs that the model uses to make decisions called?

• Supervised Learning

• Label

• Feature

• Model

Which of the following represents quantitative data?

• Brands of smartphones

• Flavors of ice cream

• Daily rainfall measurements

• Names of countries

When analyzing a scatter plot of numerical data, what can you infer if the points are closely clustered together?

• The data is random and has no relationship.

• There is no data available for analysis.

• There is a strong correlation between the two variables.

• The data is only categorical.

What is the main difference between categorical data and numerical data?

• Categorical data represents categories, while numerical data represents quantities.

• Categorical data can be measured, while numerical data cannot.

• Numerical data is always whole numbers, while categorical data can include decimals.

• Categorical data is always qualitative, while numerical data is always quantitative.