Question

A consulting company collects data on the top 500 firms in the US. For each firm they record CEO salary, annual profit, number of employees, and type of industry. They ask you to build a data science model that explains CEO salary. Is this a problem of supervised learning or unsupervised learning?

Answer 1

Building a data science model to explain CEO salary is a problem of supervised learning.

Step-by-step explanation:

Supervised Learning

Building a data science model to explain CEO salary based on the given data is a problem of supervised learning.

In supervised learning, the model is trained using labeled data, where the input data (features) and the corresponding output data (labels) are known. In this case, the input data includes CEO salary, annual profit, number of employees, and industry type, while the output data is the CEO salary.

Example: The consulting company can use a regression model, such as linear regression or decision tree regression, to predict CEO salary based on the input features.

Answer 2

supervised learning

Step-by-step explanation:

This would be considered supervised learning. This is because the data that is being collected is also being given specifically tagged category outputs such as CEO salary, annual profit, number of employees, and type of industry. These tags help the model map these factors as outputs for the collected data. Therefore, creating input-output connections for each company. If the data was not tagged and all the data was simply jumbled then it would be unsupervised learning.