Practice Evaluate the Model - 7.8 | Chapter 7: Supervised Learning – Logistic Regression | Machine Learning Basics
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7.8 - Evaluate the Model

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define the accuracy score.

💡 Hint: Think about what percentage correctly predicted out of all predictions.

Question 2

Easy

What does a true positive indicate?

💡 Hint: Consider the scenario where the model is trying to identify positives.

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 the accuracy score represent?

  • A measure of correct predictions.
  • The certainty of the model.
  • The complexity of the model.

💡 Hint: What do you think accuracy is usually based on when evaluating a model?

Question 2

True or False: A high accuracy always means the model performs well regardless of metrics.

  • True
  • False

💡 Hint: Think about how accuracy might mislead if one class is predominant.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Given the following confusion matrix results:

TP: 100 TN: 200
FP: 20 FN: 10

Calculate the accuracy, precision (TP/(TP+FP)), and recall (TP/(TP+FN)). Discuss what these metrics indicate about the model.

💡 Hint: Calculate step by step using each formula for clarity.

Question 2

How would you adjust a model if you notice a high FP rate in the confusion matrix? Propose potential strategies.

💡 Hint: Think broadly about data handling practices in machine learning.

Challenge and get performance evaluation