Practice Dataset Selection and Initial Preparation - 4.5.2.1 | Module 4: Advanced Supervised Learning & Evaluation (Weeks 8) | Machine Learning
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4.5.2.1 - Dataset Selection and Initial Preparation

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is an imbalanced dataset?

💡 Hint: Consider the context of fraud detection.

Question 2

Easy

What does imputation mean?

💡 Hint: Think about how you would handle gaps in your data.

Practice 3 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 goal of imputation in data preprocessing?

  • To fill in missing values
  • To remove entire rows of data
  • To normalize the dataset

💡 Hint: Consider what happens when data entries are incomplete.

Question 2

True or False: One-Hot Encoding is used to convert numerical features into categorical ones.

  • True
  • False

💡 Hint: Think about the direction of the conversion.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Consider a binary classification task where one class is significantly rarer than the other. How would you prepare your dataset and why?

💡 Hint: Highlighting how preprocessing aids in model generalization.

Question 2

You are given a dataset with a high number of missing values in certain features. Provide a comprehensive strategy for addressing these issues.

💡 Hint: Focus on maintaining data integrity while minimizing information loss.

Challenge and get performance evaluation