Practice Evaluate The Model (7.8) - Chapter 7: Supervised Learning – Logistic Regression

Practice: Evaluate the Model

Lesson 7.8Supervised Learning – Logistic Regression

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10 questions

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5 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
4 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
Define the accuracy score.
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Think about what percentage correctly predicted out of all predictions.
Question 2.
What does a true positive indicate?
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Consider the scenario where the model is trying to identify positives.
3Question 3.
What does the accuracy score represent?
- A measure of correct predictions.
- The certainty of the model.
- The complexity of the model.
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What do you think accuracy is usually based on when evaluating a model?
4Question 4.
True or False: A high accuracy always means the model performs well regardless of metrics.
- True
- False
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Think about how accuracy might mislead if one class is predominant.
Question 5.
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.
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Calculate step by step using each formula for clarity.
Question 6.
How would you adjust a model if you notice a high FP rate in the confusion matrix? Propose potential strategies.
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Think broadly about data handling practices in machine learning.

6 more questions in this section. Enroll free to practise them all.