8.8 - Evaluation Metrics for Deep Learning Models
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Practice Questions
Test your understanding with targeted questions
What is the formula for accuracy?
💡 Hint: Think about it as a percentage.
Name a metric used for regression tasks.
💡 Hint: Consider metrics looking at errors.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What does the F1-score evaluate?
💡 Hint: Think of it as a mean of both metrics.
True or False: RMSE is achieved by taking the square root of MSE.
💡 Hint: Focus on what the formulas indicate.
2 more questions available
Challenge Problems
Push your limits with advanced challenges
You have a dataset with 100 samples with 45 True Positives, 10 False Positives, 15 False Negatives, and 30 True Negatives. Calculate the Precision, Recall, and F1-score.
💡 Hint: Use the formulas for Precision, Recall, and then F1-score to compute each.
A regression model yields an MSE of 16 and a baseline model has an R² score of 0.6. Interpret these results and suggest improvements.
💡 Hint: Focus on understanding what MSE and R² imply for model performance.
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Reference links
Supplementary resources to enhance your learning experience.
- Evaluation Metrics for Machine Learning
- A Gentle Introduction to ROC AUC
- Understanding R²
- Cross-Validation and Evaluation Metrics in Machine Learning
- Mean Squared Error Explained
- Evaluation Metrics for Regression Models
- Understanding Accuracy, Precision, Recall in Python
- Common Evaluation Metrics Used in Machine Learning