Google

Supervised learning includes linear regression, logistic regression, and neural networks.

Examples of Supervised Learning Methods

Which option is not a supervised learning algorithm?

Logistic regression is a supervised learning algorithm.

Introductory examples of supervised learning often include linear regression, logistic regression, and _____.

Match each supervised learning method with its typical prediction output.

Order the steps for selecting a supervised learning baseline for a new prediction task.

Which group lists the supervised learning methods named in the lesson?

In supervised learning, linear regression is an unsupervised algorithm.

_____ regression estimates the probability of a class label.

Order the models from least expressive to most expressive.

Question:
A lesson on supervised learning introduces several representative algorithms. In your essay, name the three algorithms listed and explain how the lesson groups them.

Sample answer:
The lesson presents decision trees, support vector machines, and neural networks as examples of supervised learning. They are grouped together because each method learns from labeled examples and can be used to predict an output from input features. The point of the list is to show that supervised learning includes multiple kinds of models, not just one specific technique.

Key points:
- Identify decision trees as an example of supervised learning.
- Identify support vector machines as an example of supervised learning.
- Identify neural networks as an example of supervised learning.
- Explain that the lesson groups these methods together as representative supervised learning algorithms.

Rubric:
The essay must identify decision trees, support vector machines, and neural networks, and state that the lesson presents them as examples of supervised learning algorithms.

Scenario:
A team is building a churn-prediction tool for a subscription service. The project brief says the team may use only the supervised-learning methods named in the brief. The brief names three examples: linear regression, logistic regression, and neural networks.

Task:
List the methods the team is allowed to use and explain why no additional methods qualify under the brief.

Sample answer:
The permitted methods are linear regression, logistic regression, and neural networks. They are allowed because the brief explicitly names those three as the supervised-learning examples for this project. Any other algorithm would go beyond the stated limit.

Key points:
- Identify linear regression, logistic regression, and neural networks.
- Explain that the brief explicitly names those three methods as the approved supervised-learning examples.
- State that methods not listed in the brief are not allowed.

Rubric:
The answer must identify linear regression, logistic regression, and neural networks, and explain that the project rules allow only the supervised-learning examples named in the brief.

Choosing Methods from a Project Brief

Question:
A study guide names three models that are trained using labeled examples. Which models are named?

Sample answer:
It names linear regression, logistic regression, and neural networks.

Key points:
- Linear regression is one of the listed labeled-data methods.
- Logistic regression is one of the listed labeled-data methods.
- Neural networks are one of the listed labeled-data methods.

Rubric:
The response must identify linear regression, logistic regression, and neural networks.

Learn Before

Related