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Content
Evaluating and Improving Your Model
- Updated on 10/09/2024
- 450 Views
Evaluation Metrics
Accuracy:Percentage of correct predictions.
Loss:Measure of how well the model is performing.
Confusion Matrix:Shows the true positives, false positives, true negatives, and false negatives.
Improving Model Performance
Hyperparameter Tuning:Experimenting with different learning rates, batch sizes, and epochs.
Regularization:Techniques like L2 regularization and dropout to prevent overfitting.
Data Augmentation:Creating new training examples by transforming existing data (e.g., rotating images).
Example
Evaluating with a Confusion Matrix:
Import Libraries:
Generate Predictions:
Print Confusion Matrix and Classification Report:
Activity
Evaluate your deep learning model using a confusion matrix and classification report. Identify areas where the model performs well and areas for improvement.
Quiz
1. Which metric shows the percentage of correct predictions?
- a) Loss
- b) Accuracy
- c) Precision
- d) Recall
2. What does a confusion matrix display?
- a) Model loss
- b) Model accuracy
- c) True positives, false positives, true negatives, false negatives
- d) None of the above
3. Which technique is used to prevent overfitting?
- a) Increasing the learning rate
- b) Reducing the number of layers
- c) Adding dropout
- d) Using fewer epochs
4. What does hyperparameter tuning involve?
- a) Changing the model architecture
- b) Adjusting learning rates, batch sizes, etc.
- c) Evaluating the model
- d) None of the above
5. True or False: Data augmentation helps improve model performance by creating new training examples.
- a) True
- b) False
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