Monitoring model performance in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the accuracy of a model's predictions.

ML Python

accuracy = [1](y_true, y_pred)

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

Amean_squared_error

Baccuracy_score

Clog_loss

Droc_auc_score

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean_squared_error for classification accuracy

Confusing accuracy_score with log_loss

The accuracy_score function calculates the fraction of correct predictions.

2fill in blank

medium

Complete the code to plot the model's loss over epochs using matplotlib.

ML Python

plt.plot(history.history['[1]'])

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

Aval_accuracy

Baccuracy

Closs

Dval_loss

Attempts:

3 left

💡 Hint

Common Mistakes

Plotting accuracy instead of loss

Using validation keys when plotting training loss

The loss key contains the training loss values per epoch.

3fill in blank

hard

Fix the error in the code to compute the F1 score for binary classification.

ML Python

f1 = f1_score(y_true, [1], average='binary')

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Ay_pred

By_true

Cy_score

Dy_prob

Attempts:

3 left

💡 Hint

Common Mistakes

Passing true labels twice

Using predicted probabilities instead of labels

The f1_score function requires the predicted labels as the second argument.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that tracks precision and recall for each class.

ML Python

metrics = {cls: {'precision': precision_score(y_true, y_pred, pos_label=cls), 'recall': [1](y_true, y_pred, pos_label=[2])} for cls in classes}

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

Arecall_score

Bprecision_score

Ccls

Dy_pred

Attempts:

3 left

💡 Hint

Common Mistakes

Using precision_score twice

Passing predicted labels as pos_label

Use recall_score for recall and cls as the positive label for each class.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of metrics where F1 score is above 0.7.

ML Python

filtered_metrics = {cls: metrics[cls] for cls in metrics if metrics[cls]['[1]'] [2] [3]

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

Af1_score

B>

C0.7

Dprecision

Attempts:

3 left

💡 Hint

Common Mistakes

Using precision instead of f1_score

Using wrong comparison operator

Using string '0.7' instead of number

Filter classes where the f1_score is greater than 0.7.

Practice

1. Why is it important to monitor a machine learning model after deployment?

easy

A. To ensure the model continues to perform well on new data

B. To increase the model's training speed

C. To reduce the size of the model file

D. To change the model's algorithm automatically

Solution

1. Step 1: Understand model deployment context

   After deployment, models face new data that may differ from training data.

2. Step 2: Importance of monitoring

   Monitoring helps detect if the model's accuracy or other metrics drop over time.

3. Final Answer:

   To ensure the model continues to perform well on new data -> Option A

4. Quick Check:

   Monitoring = Ensure performance [OK]

Hint: Monitoring keeps models accurate after deployment [OK]

Common Mistakes:

• Confusing monitoring with training speed
• Thinking monitoring changes model algorithm automatically
• Assuming monitoring reduces model size

2. Which of the following is the correct way to calculate accuracy in Python given true labels y_true and predicted labels y_pred?

easy

A. accuracy = sum(y_true != y_pred) / len(y_true)

B. accuracy = len(y_true) / sum(y_true == y_pred)

C. accuracy = sum(y_true == y_pred) / len(y_true)

D. accuracy = sum(y_pred) / sum(y_true)

Solution

1. Step 1: Understand accuracy formula

   Accuracy = number of correct predictions divided by total predictions.

2. Step 2: Match formula to code

   sum(y_true == y_pred) counts correct predictions; dividing by len(y_true) gives accuracy.

3. Final Answer:

   accuracy = sum(y_true == y_pred) / len(y_true) -> Option C

4. Quick Check:

   Correct predictions / total = accuracy [OK]

Hint: Accuracy = correct predictions divided by total [OK]

Common Mistakes:

• Dividing total by correct instead of correct by total
• Using inequality instead of equality for correct count
• Summing predictions instead of comparing labels

3. Given the following Python code snippet monitoring model accuracy over time, what will be the printed output?

accuracies = [0.9, 0.85, 0.8, 0.75]
threshold = 0.8
alerts = [acc < threshold for acc in accuracies]
print(alerts)

medium

A. [False, False, True, True]

B. [True, True, True, True]

C. [True, False, False, False]

D. [False, False, False, True]

Solution

1. Step 1: Evaluate each accuracy against threshold

   Check if each accuracy is less than 0.8: 0.9 < 0.8? False; 0.85 < 0.8? False; 0.8 < 0.8? False; 0.75 < 0.8? True.

2. Step 2: Create alerts list

   List comprehension results in [False, False, False, True].

3. Final Answer:

   [False, False, False, True] -> Option D

4. Quick Check:

   Compare each value to threshold [OK]

Hint: Check each value against threshold in list comprehension [OK]

Common Mistakes:

• Misunderstanding < vs <= in comparisons
• Confusing True/False order in list
• Assuming 0.8 < 0.8 is True

4. You have this Python code to monitor model loss, but it raises an error. What is the error and how to fix it?

losses = [0.3, 0.25, 0.4, 0.35]
for i in range(len(losses))
    if losses[i] > 0.3:
        print(f"High loss at index {i}: {losses[i]}")

medium

A. SyntaxError due to missing colon after for loop; add ':' after 'range(len(losses))' to fix

B. TypeError because losses is not iterable; convert losses to list to fix

C. IndentationError due to wrong indentation; fix indentation of print statement

D. NameError because variable 'i' is undefined; define 'i' before loop

Solution

1. Step 1: Identify syntax error in for loop

   The for loop line is missing a colon at the end, which is required in Python syntax.

2. Step 2: Fix syntax by adding colon

   Add ':' after range(len(losses)) to correct the syntax and allow the loop to run.

3. Final Answer:

   SyntaxError due to missing colon after for loop; add ':' after 'range(len(losses))' to fix -> Option A

4. Quick Check:

   Missing colon = SyntaxError [OK]

Hint: Check colons after for/if statements [OK]

Common Mistakes:

• Ignoring missing colon causing syntax error
• Confusing indentation error with syntax error
• Assuming variable 'i' is undefined

5. You want to automate alerts when your model's F1 score drops below 0.7. Which approach best fits this monitoring task?

hard

A. Ignore F1 score and monitor only training loss during training

B. Set up a scheduled job that calculates F1 score daily and sends an alert if below 0.7

C. Manually check F1 score once a month and update the model if needed

D. Retrain the model automatically every hour regardless of performance

Solution

1. Step 1: Understand monitoring automation

   Automated scheduled jobs can regularly check model metrics like F1 score.

2. Step 2: Choose alerting based on threshold

   Sending alerts when F1 score drops below 0.7 helps catch performance issues early.

3. Final Answer:

   Set up a scheduled job that calculates F1 score daily and sends an alert if below 0.7 -> Option B

4. Quick Check:

   Automate checks + alerts = Set up a scheduled job that calculates F1 score daily and sends an alert if below 0.7 [OK]

Hint: Automate metric checks and alert on threshold breach [OK]

Common Mistakes:

• Retraining without checking performance first
• Manual checks too infrequent for timely alerts
• Ignoring key metrics like F1 score