You need to predict the income range of a given customer by using the following dataset. Which two fields should you use as features? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
Think of Features as the Clues to the mystery and the Label as the Solution to that mystery. First and Last Name won't tell you anything when it comes to predicting the income. You already know what the label is because it is what your trying to solve for. The clues (Features) to the mystery is Age + Education Level = Income Range Solution to the Mystery (Label).
AC is the answer.
https://learn.microsoft.com/en-us/training/modules/create-regression-model-azure-machine-learning-designer/2-regression-scenarios
Regression is a form of machine learning used to understand the relationships between variables to predict a desired outcome. Regression predicts a numeric label or outcome based on variables, or features. For example, an automobile sales company might use the characteristics of a car (such as engine size, number of seats, mileage, and so on) to predict its likely selling price. In this case, the characteristics of the car are the features, and the selling price is the label.
In a world where employees with caucasian sounding names more so like John, David (male and white), etc occupy a lot of executive level positions, I am a little curious to know if first names do play a part in predicting incomes :)
In practical life the more you age, the more experience that you'll get and more income vs. a young age (newly graduates) with no experience and should be low income? :)
kidding aside. This is just a comment to the table example. xD
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