Evaluation of fine-tuned models in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main purpose of evaluating a fine-tuned model?

To check how well the model performs on new, unseen data after training, ensuring it learned useful patterns and can make accurate predictions.

beginner

Name two common metrics used to evaluate classification models.

Accuracy and F1-score are common metrics. Accuracy measures the percentage of correct predictions, while F1-score balances precision and recall.

intermediate

Why is it important to use a separate test set when evaluating a fine-tuned model?

Using a separate test set helps measure how the model performs on data it has never seen before, preventing overly optimistic results from training data.

intermediate

What does overfitting mean in the context of fine-tuned models?

Overfitting happens when a model learns the training data too well, including noise, and performs poorly on new data.

intermediate

How can you visually inspect a fine-tuned model's performance on classification tasks?

By using a confusion matrix, which shows correct and incorrect predictions for each class, helping identify where the model makes mistakes.

Which metric is best to use when classes are imbalanced?

A Accuracy

B F1-score

C Training loss

D Epoch count

F1-score balances precision and recall, making it better for imbalanced classes than accuracy.

What does a high training accuracy but low test accuracy usually indicate?

A Good generalization

B Underfitting

C Data leakage

D Overfitting

High training accuracy but low test accuracy means the model learned training data too well but fails on new data, which is overfitting.

Which dataset is used to tune model parameters during fine-tuning?

A Training set

B Validation set

C Test set

D Unlabeled data

The training set is used to adjust model parameters during fine-tuning.

What is the role of the test set in model evaluation?

A To assess final model performance

B To tune hyperparameters

C To train the model

D To generate synthetic data

The test set is used only to assess the final performance of the model on unseen data.

Which visualization helps understand classification errors?

A Scatter plot

B Loss curve

C Confusion matrix

D Histogram

A confusion matrix shows how many predictions were correct or wrong for each class.

Explain why evaluating a fine-tuned model on unseen data is crucial and describe common metrics used.

Think about how to know if the model learned well beyond just memorizing.

Describe overfitting in fine-tuned models and how you can detect it using evaluation results.

Consider what happens when a model performs great on training but poorly on new data.

Practice

1. What is the main purpose of evaluating a fine-tuned model?

easy

A. To reduce the number of model layers

B. To check how well the model performs on new, unseen data

C. To speed up the training process

D. To increase the size of the training dataset

Solution

1. Step 1: Understand model evaluation

   Evaluation measures how well the model predicts on data it has not seen before.

2. Step 2: Identify the purpose of evaluation

   It helps us know if the model learned useful patterns or just memorized training data.

3. Final Answer:

   To check how well the model performs on new, unseen data -> Option B

4. Quick Check:

   Evaluation = performance on new data [OK]

Hint: Evaluation checks model on new data, not training data [OK]

Common Mistakes:

• Confusing evaluation with training
• Thinking evaluation changes model structure
• Believing evaluation increases data size

2. Which of the following is the correct way to evaluate a fine-tuned model in Python using TensorFlow?

easy

A. model.compile(optimizer='adam')

B. model.train(test_data, test_labels)

C. model.predict(train_data)

D. model.evaluate(test_data, test_labels)

Solution

1. Step 1: Recall TensorFlow evaluation method

   TensorFlow models use model.evaluate() to measure performance on test data.

2. Step 2: Identify correct usage

   model.evaluate(test_data, test_labels) returns loss and metrics on unseen data.

3. Final Answer:

   model.evaluate(test_data, test_labels) -> Option D

4. Quick Check:

   Use model.evaluate() for testing [OK]

Hint: Use model.evaluate() with test data for evaluation [OK]

Common Mistakes:

• Using model.train() instead of evaluate
• Calling predict() without labels for evaluation
• Confusing compile() with evaluation

3. Given the code below, what will be the output of print(loss, accuracy)?

loss, accuracy = model.evaluate(x_test, y_test)
print(loss, accuracy)

medium

A. The loss value and accuracy metric on the test set

B. The training loss and accuracy values

C. A syntax error because evaluate returns only one value

D. The predicted labels for x_test

Solution

1. Step 1: Understand model.evaluate() output

   It returns loss and metrics (like accuracy) on the test data.

2. Step 2: Analyze the print statement

   Printing loss, accuracy shows these two values from evaluation.

3. Final Answer:

   The loss value and accuracy metric on the test set -> Option A

4. Quick Check:

   evaluate() returns loss and accuracy [OK]

Hint: model.evaluate() returns loss and metrics tuple [OK]

Common Mistakes:

• Thinking evaluate returns training metrics
• Assuming evaluate returns predictions
• Believing evaluate returns only one value

4. You ran model.evaluate(x_test) but got an error. What is the likely cause?

medium

A. The model is not compiled

B. The test data x_test is empty

C. Missing the true labels y_test in the evaluate call

D. The model has too many layers

Solution

1. Step 1: Check evaluate method requirements

   model.evaluate() needs both input data and true labels to compute metrics.

2. Step 2: Identify missing argument

   Calling model.evaluate(x_test) misses y_test, causing an error.

3. Final Answer:

   Missing the true labels y_test in the evaluate call -> Option C

4. Quick Check:

   evaluate() needs inputs and labels [OK]

Hint: Always pass both data and labels to evaluate() [OK]

Common Mistakes:

• Forgetting to pass labels to evaluate()
• Assuming evaluate works with inputs only
• Ignoring model compilation status

5. You fine-tuned two models and got these evaluation results on the same test set:

• Model A: loss=0.25, accuracy=0.90
• Model B: loss=0.20, accuracy=0.85

Which model should you choose and why?

hard

A. Model A, because it has higher accuracy which is more important than loss

B. Model B, because it has lower loss indicating better overall fit

C. Model A, because loss and accuracy must both be higher

D. Model B, because accuracy is less important than loss

Solution

1. Step 1: Understand evaluation metrics

   Accuracy shows correct predictions percentage; loss shows error magnitude.

2. Step 2: Compare models on accuracy and loss

   Model A has higher accuracy (0.90) but slightly higher loss (0.25) than Model B.

3. Step 3: Decide based on goal

   For classification, accuracy is usually more important to pick the better model.

4. Final Answer:

   Model A, because it has higher accuracy which is more important than loss -> Option A

5. Quick Check:

   Higher accuracy preferred for classification [OK]

Hint: Pick model with higher accuracy for classification tasks [OK]

Common Mistakes:

• Choosing model with lower loss but worse accuracy
• Ignoring accuracy when loss differs
• Assuming loss always trumps accuracy