Practice Step 3: Feature Selection And Splitting (9.4) - Chapter 9: End-to-End Machine Learning Project – Predicting Student Exam Performance

Practice: Step 3: Feature Selection and Splitting

Lesson 9.4End-to-End Machine Learning Project – Predicting Student Exam Performance

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8 questions

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6 sample questions

Question 1.
What is a label in the context of machine learning?
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Think about what we are trying to find out with our model.
Question 2.
Why do we separate features from labels?
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Remember, we want the model to learn from data without knowing the result initially.
3Question 3.
What is the primary purpose of feature selection?
- To increase dataset size.
- To improve model accuracy by selecting relevant features.
- To convert categorical variables to numerical.
- To visualize data.
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Think about what can make a machine learning model more effective.
4Question 4.
True or False: Splitting the dataset into training and testing sets helps prevent overfitting.
- True
- False
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What happens if you train and test on the same data?
Question 5.
Imagine you have a dataset with a large number of features. How would you approach the feature selection process for building a model?
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Consider which features have the most significant impact on the outcome.
Question 6.
Given a dataset of 500 instances with both features and labels, explain how you’d choose an appropriate ratio for a train-test split.
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Think about sample size and how to balance training and evaluation.

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