Practice Step 3: Feature Selection and Splitting - 9.4 | Chapter 9: End-to-End Machine Learning Project – Predicting Student Exam Performance | Machine Learning Basics
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9.4 - Step 3: Feature Selection and Splitting

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is a label in the context of machine learning?

💡 Hint: Think about what we are trying to find out with our model.

Question 2

Easy

Why do we separate features from labels?

💡 Hint: Remember, we want the model to learn from data without knowing the result initially.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

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.

💡 Hint: Think about what can make a machine learning model more effective.

Question 2

True or False: Splitting the dataset into training and testing sets helps prevent overfitting.

  • True
  • False

💡 Hint: What happens if you train and test on the same data?

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Imagine you have a dataset with a large number of features. How would you approach the feature selection process for building a model?

💡 Hint: Consider which features have the most significant impact on the outcome.

Question 2

Given a dataset of 500 instances with both features and labels, explain how you’d choose an appropriate ratio for a train-test split.

💡 Hint: Think about sample size and how to balance training and evaluation.

Challenge and get performance evaluation