Evaluation of fine-tuned models in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Metrics

intermediate

Understanding evaluation metrics for fine-tuned classification models

You fine-tuned a text classification model and evaluated it on a test set. The model predicted labels for 100 samples. The confusion matrix is:

[[40, 10], [5, 45]]

What is the accuracy of the model?

A 0.90

B 0.85

C 0.80

D 0.75

💡 Hint

Accuracy = (True Positives + True Negatives) / Total samples

Accuracy = (40 + 45) / 100 = 85 / 100 = 0.85. But check carefully: 40 + 45 = 85, so accuracy is 0.85, not 0.90.

❓ Predict Output

intermediate

Output of evaluation code for fine-tuned regression model

Consider this Python code evaluating a fine-tuned regression model's predictions:

from sklearn.metrics import mean_squared_error
true = [3.0, -0.5, 2.0, 7.0]
pred = [2.5, 0.0, 2.0, 8.0]
mse = mean_squared_error(true, pred)
print(round(mse, 2))

What is the printed output?

A 0.38

B 0.50

C 0.75

D 1.25

💡 Hint

Mean Squared Error is the average of squared differences between true and predicted values.

Squared errors: (3-2.5)^2=0.25, (-0.5-0)^2=0.25, (2-2)^2=0, (7-8)^2=1. Sum=1.5, average=1.5/4=0.375, rounded to 0.38.

❓ Model Choice

advanced

Choosing the best evaluation metric for imbalanced fine-tuned models

You fine-tuned a model on a dataset where 95% of samples belong to class A and 5% to class B. Which evaluation metric is best to assess the model's performance on the minority class?

A Accuracy

B Precision

C F1-score

D Recall

💡 Hint

Consider a metric that balances precision and recall for the minority class.

F1-score balances precision and recall, making it suitable for imbalanced data to evaluate minority class performance.

🔧 Debug

advanced

Identifying the error in evaluation code for fine-tuned model

What error will this code raise when evaluating a fine-tuned classification model?

from sklearn.metrics import accuracy_score
true_labels = [1, 0, 1, 1]
pred_labels = [1, 0, 0]
acc = accuracy_score(true_labels, pred_labels)
print(acc)

A No error, prints accuracy

B TypeError: unsupported operand type(s) for +: 'int' and 'str'

C IndexError: list index out of range

D ValueError: Found input variables with inconsistent numbers of samples

💡 Hint

Check if true and predicted label lists have the same length.

accuracy_score requires true and predicted labels to have the same length; here lengths differ (4 vs 3), causing ValueError.

🧠 Conceptual

expert

Impact of fine-tuning on model evaluation metrics

After fine-tuning a pre-trained language model on a small dataset, you observe that the training accuracy is very high but the validation accuracy is low. What is the most likely explanation?

A The model is overfitting the training data

B The model is underfitting the training data

C The validation data is corrupted

D The learning rate is too low

💡 Hint

Think about what happens when a model learns training data too well but fails on new data.

High training accuracy but low validation accuracy indicates overfitting, where the model memorizes training data but does not generalize well.

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