Practice Confusion Matrix (28.4.5) - Introduction to Model Evaluation

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6 sample questions

Question 1.
What does a True Positive (TP) signify in a confusion matrix?
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Think of it as right answers for positive cases.
Question 2.
Explain what a False Positive (FP) is.
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Consider it as a wrong answer for a positive case.
3Question 3.
What is the purpose of a confusion matrix?
- To visualize data
- To evaluate model performance
- To train models
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Consider what tools are used after training a machine learning model.
4Question 4.
True or False: True Negatives represent the number of negative classes that were incorrectly predicted as positive.
- True
- False
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Remember what 'true' means in this context.
Question 5.
Consider a model's confusion matrix: TP = 25, TN = 50, FP = 5, FN = 20. What is the accuracy, precision, and recall?
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Calculate each metric using their respective formulas.
Question 6.
Discuss how you would improve a classification model if the confusion matrix shows a high number of False Negatives.
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Think about adjustments that could help the model learn better.

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