Automated evaluation metrics in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate accuracy from predictions and labels.

Prompt Engineering / GenAI

accuracy = sum(predictions == [1]) / len(predictions)

Drag options to blanks, or click blank then click option'

Atargets

Bpredictions

Coutputs

Dlabels

Attempts:

3 left

💡 Hint

Common Mistakes

Using predictions instead of labels for comparison

Dividing by wrong length

Accuracy compares predicted labels to the true labels.

2fill in blank

medium

Complete the code to compute precision score using sklearn.

Prompt Engineering / GenAI

precision = precision_score([1], predictions)

Drag options to blanks, or click blank then click option'

Atargets

Blabels

Coutputs

Dpredictions

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping predictions and labels

Using wrong variable names

Precision compares true labels (first argument) to predictions.

3fill in blank

hard

Fix the error in computing F1 score by filling the missing argument.

Prompt Engineering / GenAI

f1 = f1_score(labels, predictions, average=[1])

Drag options to blanks, or click blank then click option'

A'binary'

B'micro'

C'weighted'

D'macro'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'macro' or 'micro' for binary tasks

Omitting the average argument

For binary classification, average='binary' is required.

4fill in blank

hard

Fill both blanks to create a dictionary of recall scores for each class.

Prompt Engineering / GenAI

recall_scores = {cls: recall_score(labels, predictions, average=[1], labels=[cls]) for cls in [2]

Drag options to blanks, or click blank then click option'

A'binary'

B'macro'

C'weighted'

Dclasses

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'macro' average which averages all classes

Not iterating over class labels

Recall per class uses average='binary' and iterates over classes.

5fill in blank

hard

Fill all three blanks to compute a confusion matrix and extract true positives.

Prompt Engineering / GenAI

cm = confusion_matrix([1], [2])
true_positives = cm[[3], [3]]

Drag options to blanks, or click blank then click option'

Alabels

Bpredictions

C1

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping labels and predictions

Using wrong index for true positives

The confusion matrix compares true labels and predictions. True positives for class 0 are at cm[0, 0].

Practice

1. Which automated evaluation metric is commonly used to measure the accuracy of classification models?

easy

A. Perplexity

B. Mean Squared Error

C. BLEU Score

D. Accuracy

Solution

1. Step 1: Understand classification metrics

   Classification models predict categories, so metrics like Accuracy measure correct predictions over total predictions.

2. Step 2: Match metric to task

   Mean Squared Error is for regression, BLEU and Perplexity are for language tasks, so Accuracy fits classification best.

3. Final Answer:

   Accuracy -> Option D

4. Quick Check:

   Classification accuracy = Accuracy [OK]

Hint: Accuracy measures correct predictions in classification [OK]

Common Mistakes:

• Confusing regression metrics with classification
• Using BLEU for classification tasks
• Mixing Perplexity with accuracy

2. Which of the following is the correct Python syntax to calculate accuracy using scikit-learn?

easy

A. accuracy = accuracy(y_true, y_pred)

B. accuracy = score_accuracy(y_true, y_pred)

C. accuracy = accuracy_score(y_true, y_pred)

D. accuracy = calc_accuracy(y_true, y_pred)

Solution

1. Step 1: Recall scikit-learn function name

   The correct function to compute accuracy is accuracy_score from sklearn.metrics.

2. Step 2: Check function call syntax

   It requires two arguments: true labels and predicted labels, called as accuracy_score(y_true, y_pred).

3. Final Answer:

   accuracy = accuracy_score(y_true, y_pred) -> Option C

4. Quick Check:

   scikit-learn accuracy function = accuracy_score [OK]

Hint: Use accuracy_score from sklearn.metrics for accuracy [OK]

Common Mistakes:

• Using incorrect function names
• Missing import of accuracy_score
• Swapping argument order

3. Given the following code snippet, what will be the printed F1 score?

from sklearn.metrics import f1_score

y_true = [1, 0, 1, 1, 0]
y_pred = [1, 0, 0, 1, 0]
f1 = f1_score(y_true, y_pred)
print(round(f1, 2))

medium

A. 0.80

B. 0.75

C. 0.67

D. 0.60

Solution

1. Step 1: Calculate precision and recall

   True positives (TP) = 2 (positions 0 and 3), False positives (FP) = 0, False negatives (FN) = 1 (position 2).

2. Step 2: Compute F1 score

   Precision = TP / (TP + FP) = 2/2 = 1.0; Recall = TP / (TP + FN) = 2/3 ≈ 0.67; F1 = 2 * (Precision * Recall) / (Precision + Recall) ≈ 2*(1*0.67)/(1+0.67) ≈ 0.80.

3. Step 3: Verify scikit-learn default behavior

   By default, f1_score uses 'binary' average, so calculation matches above.

4. Step 4: Check rounding

   Rounded to two decimals, the printed value is 0.80, but the actual f1_score value is approximately 0.80.

5. Final Answer:

   0.80 -> Option A

6. Quick Check:

   F1 score = 0.80 [OK]

Hint: F1 balances precision and recall; calculate both first [OK]

Common Mistakes:

• Confusing precision with recall
• Rounding too early
• Ignoring default average parameter

4. You run this code but get an error:

from sklearn.metrics import precision_score

true = [1, 0, 1]
pred = [1, 1, 0]
score = precision_score(true, pred)
print(score)

What is the likely cause of the error?

medium

A. No error; code runs fine

B. Mismatch in label types causing undefined precision

C. Incorrect variable names used in function call

D. Missing import of precision_score

Solution

1. Step 1: Check imports and variables

   precision_score is imported correctly and variables true, pred are defined properly.

2. Step 2: Understand precision_score behavior

   Precision is undefined if there are no predicted positives for the positive class, which can cause warnings or errors.

3. Step 3: Analyze given data

   pred has one positive (1), true has positives at positions 0 and 2; so precision can be computed without error.

4. Step 4: Consider label types

   If labels are not binary or have unexpected types, precision_score may error; here labels are fine, so no error expected.

5. Final Answer:

   No error; code runs fine -> Option A

6. Quick Check:

   Code runs fine with correct inputs [OK]

Hint: Check label types and predicted positives for precision errors [OK]

Common Mistakes:

• Assuming import errors without checking
• Confusing variable names
• Ignoring label format requirements

5. You want to evaluate a language generation model. Which automated metric should you choose to measure how well the model's output matches human references?

hard

A. Mean Absolute Error

B. BLEU Score

C. Accuracy

D. Silhouette Score

Solution

1. Step 1: Identify task type

   Language generation models produce text outputs, so evaluation needs to compare generated text to reference text.

2. Step 2: Match metric to task

   BLEU Score measures overlap of n-grams between generated and reference text, widely used for language generation evaluation.

3. Step 3: Exclude unrelated metrics

   Mean Absolute Error is for regression, Accuracy for classification, Silhouette Score for clustering, so they don't fit language generation.

4. Final Answer:

   BLEU Score -> Option B

5. Quick Check:

   Language generation evaluation = BLEU Score [OK]

Hint: Use BLEU for comparing generated text to references [OK]

Common Mistakes:

• Using regression or classification metrics for text
• Confusing clustering metrics with language metrics
• Ignoring task-specific metric choice