Output format control in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to print the model's predictions as a list.

Prompt Engineering / GenAI

predictions = model.predict(data)
print(list([1]))

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

Adata

Bpredictions

Cmodel

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'data' instead of 'predictions' inside print.

Trying to print 'model' object directly.

The model's predictions are stored in the variable 'predictions'. To print them as a list, we convert 'predictions' to a list.

2fill in blank

medium

Complete the code to calculate accuracy score from true and predicted labels.

Prompt Engineering / GenAI

from sklearn.metrics import accuracy_score
accuracy = accuracy_score([1], predicted_labels)

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

Atrue_labels

Bpredicted_labels

Cmodel

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping true and predicted labels in accuracy_score.

Passing the model object instead of labels.

The accuracy_score function compares true labels with predicted labels. The first argument must be the true labels.

3fill in blank

hard

Fix the error in printing the training loss after each epoch.

Prompt Engineering / GenAI

for epoch in range(5):
    loss = model.train_on_batch(x_train, y_train)
    print(f"Epoch {epoch+1}, Loss: [1]")

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

Aepoch

Bmodel

Closs

Dx_train

Attempts:

3 left

💡 Hint

Common Mistakes

Printing 'model' or 'epoch' instead of 'loss'.

Forgetting to add 1 to epoch for display.

The variable 'loss' holds the training loss value returned by train_on_batch, so it should be printed.

4fill in blank

hard

Fill both blanks to create a dictionary of predictions with keys as sample IDs and values as predicted labels.

Prompt Engineering / GenAI

pred_dict = [1](sample_id: [2] for sample_id, [3] in zip(ids, predictions))

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

Adict

Blabel

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'list' instead of 'dict' for dictionary creation.

Using wrong variable names for predicted labels.

To create a dictionary comprehension, use 'dict' and map sample_id to label from predictions.

5fill in blank

hard

Fill all three blanks to filter predictions greater than threshold and create a result dictionary.

Prompt Engineering / GenAI

result = [1]: [2] for [3], [4] in enumerate(predictions) if [2] > threshold}

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

Ai

Bpred

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing variable names or forgetting to use enumerate.

Using wrong variable names for keys or values.

Use 'i' as the key from enumerate, 'pred' as the value, and iterate with 'i, pred' to filter predictions above threshold.