ROC curve and AUC in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function that calculates the ROC curve.

ML Python

from sklearn.metrics import [1]

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

Amean_squared_error

Baccuracy_score

Cconfusion_matrix

Droc_curve

Attempts:

3 left

💡 Hint

Common Mistakes

Importing accuracy_score instead of roc_curve.

Confusing roc_curve with confusion_matrix.

The roc_curve function is used to compute the false positive rate and true positive rate needed to plot the ROC curve.

2fill in blank

medium

Complete the code to calculate the false positive rate and true positive rate for the ROC curve.

ML Python

fpr, tpr, thresholds = roc_curve(y_true, [1])

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

Ay_pred

By_scores

Cy_labels

Dy_train

Attempts:

3 left

💡 Hint

Common Mistakes

Using predicted class labels instead of scores.

Using training labels instead of predictions.

The ROC curve requires the predicted scores or probabilities, not the predicted classes.

3fill in blank

hard

Fix the error in the code to calculate the AUC score.

ML Python

auc_score = [1](y_true, y_scores)

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

Aroc_auc_score

Baccuracy_score

Croc_curve

Dconfusion_matrix

Attempts:

3 left

💡 Hint

Common Mistakes

Using roc_curve instead of roc_auc_score.

Using accuracy_score which measures accuracy, not AUC.

The roc_auc_score function calculates the area under the ROC curve, which measures the model's ability to distinguish classes.

4fill in blank

hard

Fill both blanks to plot the ROC curve using matplotlib.

ML Python

plt.plot([1], [2], label='ROC curve')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve')
plt.legend()

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

Afpr

Btpr

Cthresholds

Dy_true

Attempts:

3 left

💡 Hint

Common Mistakes

Plotting thresholds instead of rates.

Swapping true positive rate and false positive rate.

The ROC curve plots false positive rate (fpr) on the x-axis and true positive rate (tpr) on the y-axis.

5fill in blank

hard

Fill all three blanks to compute and print the AUC score after plotting the ROC curve.

ML Python

auc = [1](y_true, [2])
print(f'AUC score: [3]')

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

Aroc_auc_score

By_scores

Cauc

Droc_curve

Attempts:

3 left

💡 Hint

Common Mistakes

Printing the function name instead of the variable.

Using predicted labels instead of scores.

First, compute the AUC score using roc_auc_score with true labels and predicted scores. Then print the computed auc variable.

Practice

1. What does the ROC curve primarily show in a classification model?

easy

A. The trade-off between true positive rate and false positive rate at different thresholds

B. The accuracy of the model on the training data

C. The number of correct predictions made by the model

D. The loss value during model training

Solution

1. Step 1: Understand ROC curve components

   The ROC curve plots the true positive rate (sensitivity) against the false positive rate (1 - specificity) at various threshold settings.

2. Step 2: Identify what ROC shows

   This curve shows how well the model can distinguish between classes by varying the decision threshold.

3. Final Answer:

   The trade-off between true positive rate and false positive rate at different thresholds -> Option A

4. Quick Check:

   ROC curve = True Positive Rate vs False Positive Rate [OK]

Hint: ROC curve plots TPR vs FPR at thresholds [OK]

Common Mistakes:

• Confusing ROC with accuracy
• Thinking ROC shows loss values
• Assuming ROC shows only one threshold

2. Which of the following Python code snippets correctly imports the function to compute AUC from sklearn?

easy

A. import roc_auc_score from sklearn.metrics

B. from sklearn.metrics import auc_roc_score

C. from sklearn.metrics import roc_auc_score

D. import sklearn.metrics.roc_auc_score

Solution

1. Step 1: Recall correct import syntax

   In Python, functions are imported using 'from module import function' syntax.

2. Step 2: Identify correct function name

   The correct function name to compute AUC is 'roc_auc_score' from 'sklearn.metrics'.

3. Final Answer:

   from sklearn.metrics import roc_auc_score -> Option C

4. Quick Check:

   Correct import syntax = from sklearn.metrics import roc_auc_score [OK]

Hint: Use 'from sklearn.metrics import roc_auc_score' [OK]

Common Mistakes:

• Using wrong import syntax
• Misspelling function name
• Trying to import module as function

3. Given the following code, what is the printed AUC value?

from sklearn.metrics import roc_auc_score
true_labels = [0, 0, 1, 1]
predicted_scores = [0.1, 0.4, 0.35, 0.8]
auc = roc_auc_score(true_labels, predicted_scores)
print(round(auc, 2))

medium

A. 0.75

B. 0.85

C. 0.65

D. 0.95

Solution

1. Step 1: Understand roc_auc_score calculation

   The AUC measures how well the predicted scores rank positive samples higher than negative ones.

2. Step 2: Calculate AUC for given data

   Using the pairs, the AUC is 0.75 as the positive samples are ranked correctly most of the time but not perfectly.

3. Final Answer:

   0.75 -> Option A

4. Quick Check:

   AUC for given scores = 0.75 [OK]

Hint: Use roc_auc_score to get AUC directly [OK]

Common Mistakes:

• Confusing AUC with accuracy
• Rounding incorrectly
• Mixing up true labels and scores

4. Identify the error in the following code that tries to plot an ROC curve:

from sklearn.metrics import roc_curve
fpr, tpr, thresholds = roc_curve(y_true, y_pred)
plt.plot(fpr, tpr)
plt.show()

medium

A. roc_curve function is not imported correctly

B. y_pred should be predicted probabilities, not class labels

C. plt.plot should use thresholds instead of fpr

D. Missing import statement for numpy

Solution

1. Step 1: Check input requirements for roc_curve

   The roc_curve function requires true labels and predicted scores or probabilities, not class labels.

2. Step 2: Identify the error in inputs

   Using predicted class labels instead of probabilities causes incorrect ROC curve calculation.

3. Final Answer:

   y_pred should be predicted probabilities, not class labels -> Option B

4. Quick Check:

   Use probabilities for roc_curve inputs [OK]

Hint: Use predicted probabilities, not labels, for roc_curve [OK]

Common Mistakes:

• Passing class labels instead of probabilities
• Forgetting to import matplotlib.pyplot as plt
• Plotting thresholds instead of FPR and TPR

5. You have two models with these AUC scores: Model A = 0.82, Model B = 0.79. You want to improve Model B's performance. Which approach is best to increase the AUC score?

hard

A. Reduce the size of the training dataset to avoid overfitting

B. Tune the classification threshold to balance true positive and false positive rates

C. Use accuracy as the main metric instead of AUC

D. Increase the number of training epochs without changing data or model

Solution

1. Step 1: Understand what affects AUC

   AUC measures the model's ranking ability and is threshold-independent.

2. Step 2: Choose the best method to improve AUC

   Tuning the classification threshold does not improve AUC because AUC is threshold-independent; instead, improving the model's discriminative ability or training is needed.

3. Step 3: Evaluate options

   Increasing training epochs (Increase the number of training epochs without changing data or model) can help but may cause overfitting if not managed properly. Reducing dataset size (Reduce the size of the training dataset to avoid overfitting) usually harms performance. Using accuracy (Use accuracy as the main metric instead of AUC) does not improve AUC. Tuning threshold (Tune the classification threshold to balance true positive and false positive rates) affects operating point but not AUC.

4. Final Answer:

   Increase the number of training epochs without changing data or model -> Option D

5. Quick Check:

   More epochs can improve model performance and AUC [OK]

Hint: More training epochs improve AUC [OK]

Common Mistakes:

• Thinking threshold tuning affects AUC
• Reducing data size harms performance
• Switching to accuracy doesn't improve AUC