Practice Model Evaluation Techniques - 5.8 | 5. Supervised Learning – Advanced Algorithms | Data Science Advance
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5.8 - Model Evaluation Techniques

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does k-fold cross-validation help us determine?

💡 Hint: Think about testing the model on different parts of the dataset.

Question 2

Easy

Name one advantage of using a confusion matrix.

💡 Hint: Consider what details about classified outcomes it includes.

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 does k-fold cross-validation involve?

  • Dividing the dataset into multiple subsets and training on them repeatedly
  • Using the entire dataset for training
  • Randomly selecting one subset for validation

💡 Hint: Remember how the whole data is utilized.

Question 2

True or False: Higher ROC-AUC values are indicative of poor model performance.

  • True
  • False

💡 Hint: Think about the definition of ROC-AUC.

Solve 3 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Consider a scenario where you have a model that predicts heart disease based on several features. You've calculated an F1-score of 0.75. Given this value, what can you infer about your model's performance?

💡 Hint: Reflect on what F1-score balances between for classification tasks.

Question 2

If your model has an R² value of 0.9, how would you interpret this in the context of a housing price prediction model?

💡 Hint: Think about how this reflects on the model’s explanatory power.

Challenge and get performance evaluation