Practice Making Predictions (6.7) - Chapter 6: Supervised Learning – Linear Regression

Practice: Making Predictions

Lesson 6.7Supervised Learning – Linear Regression

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8 questions

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6 sample questions

Question 1.
What is the input needed to predict a person's salary if they have 4 years of experience?
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Think of the years of experience.
Question 2.
Can predictions be made without a trained model?
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Recall the role of training in supervised learning.
3Question 3.
What do you input into the model for making a salary prediction?
- Years of Experience
- Salary
- Employee ID
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Consider what variable affects salary directly.
4Question 4.
The output of the model is known as...?
- Predicted value
- Training label
- Input feature
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Think about the terminology used in predictions.
Question 5.
Suppose a model trained to predict salaries based on years of experience outputs a salary of $90,000 for 10 years of experience. If real-world data shows the average salary is $75,000, discuss two potential reasons for this discrepancy.
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Consider both model limitations and real-world variables.
Question 6.
You developed a model that predicts salaries unusually high compared to known metrics. Suggest a method for validating your predictions and improving accuracy.
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Think about steps you can take to assess model robustness.

4 more questions in this section. Enroll free to practise them all.