Practice Confusion Matrix - 12.5.C | 12. Model Evaluation and Validation | Data Science Advance
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Confusion Matrix

12.5.C - Confusion Matrix

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Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

Define True Positives.

💡 Hint: Think about what happens when the model gets a positive case right.

Question 2 Easy

What does a False Positive indicate?

💡 Hint: Consider the mistakes a model could make in predictions.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does a confusion matrix help to visualize?

Classification results
Regression results
Data pre-processing

💡 Hint: Think about what category of models uses confusion matrices.

Question 2

True or False: True Negatives (TN) indicates correct predictions of negative cases.

True
False

💡 Hint: Consider how the terms are defined.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

You are tasked with improving a classification model for a credit approval system. The confusion matrix shows high false negatives. Discuss strategies to reduce them and enhance the model's recall.

💡 Hint: Consider aspects of model training and evaluation.

Challenge 2 Hard

Given a confusion matrix with TP = 40, FP = 25, FN = 15, and TN = 20, calculate precision, recall, and the F1 Score. Discuss how these metrics could inform model improvement.

💡 Hint: Make sure to follow through the calculations accurately.

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