Practice Evaluating Classification Models - 4 | Classification Algorithms | Data Science Basic
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4 - Evaluating Classification Models

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does True Positive mean in a confusion matrix?

💡 Hint: Think about what 'true' indicates in terms of predictions.

Question 2

Easy

Define Accuracy in terms of classification performance.

💡 Hint: It's a simple formula involving TP and TN.

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 a True Positive indicate in the context of a confusion matrix?

  • Correctly predicted negative instances
  • Correctly predicted positive instances
  • Incorrectly predicted positive instances

💡 Hint: Focus on what 'True' and 'Positive' mean in this context.

Question 2

True or False: F1-Score is calculated as the average of Precision and Recall.

  • True
  • False

💡 Hint: Recall the specific formula for F1-Score.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You have a confusion matrix showing TP=90, FP=30, TN=50, and FN=10. Discuss the implications of these values on model performance and calculate the Precision, Recall, and F1-Score.

💡 Hint: Apply formulas directly and consider real-world consequences.

Question 2

Consider a scenario where the confusion matrix reveals a high accuracy but low recall. What might this indicate about the model, and how could it be adjusted?

💡 Hint: Backtrack through your definitions of precision and recall to connect them.

Challenge and get performance evaluation