ROC and AUC curves in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function that computes the ROC AUC score.

TensorFlow

from sklearn.metrics import [1]

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

Amean_squared_error

Broc_auc_score

Caccuracy_score

Dconfusion_matrix

Attempts:

3 left

💡 Hint

Common Mistakes

Importing accuracy_score instead of roc_auc_score

Importing confusion_matrix which is unrelated to ROC AUC

Importing mean_squared_error which is for regression

The roc_auc_score function calculates the area under the ROC curve, which measures how well a model distinguishes between classes.

2fill in blank

medium

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

TensorFlow

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

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

Athresholds

By_true

Cy_pred

Dy_scores

Attempts:

3 left

💡 Hint

Common Mistakes

Passing true labels instead of predicted scores

Passing predicted class labels instead of scores

The roc_curve function requires the true labels and the predicted scores (probabilities) to compute the ROC points.

3fill in blank

hard

Fix the error in this TensorFlow code to compute AUC metric during model compilation.

TensorFlow

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=[[1]])

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

Atf.keras.metrics.AUC()

Btf.keras.metrics.Accuracy()

Ctf.keras.metrics.Precision()

Dtf.keras.metrics.Recall()

Attempts:

3 left

💡 Hint

Common Mistakes

Using Accuracy metric instead of AUC

Using Precision or Recall which are different metrics

To track AUC during training in TensorFlow, use tf.keras.metrics.AUC() as a metric.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps thresholds to TPR values from ROC curve data.

TensorFlow

threshold_tpr = {thr[1] tpr[2] for thr, tpr in zip(thresholds, tpr)}

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

A:

B,

C.

D=

Attempts:

3 left

💡 Hint

Common Mistakes

Using comma instead of colon between key and value

Using equals sign instead of colon

Adding dot after variable names incorrectly

In dictionary comprehensions, key and value are separated by a colon :. To access the value of tpr, use the variable directly. The correct syntax is thr: tpr.

5fill in blank

hard

Fill all three blanks to create a TensorFlow callback that stops training when AUC reaches 0.95 or higher.

TensorFlow

early_stop = tf.keras.callbacks.EarlyStopping(monitor='[1]', patience=[2], mode='[3]')

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

Aauc

Bval_loss

Cval_auc

Daccuracy

E5

F10

Gmin

Hmax

Attempts:

3 left

💡 Hint

Common Mistakes

Monitoring 'val_loss' instead of 'val_auc'

Setting patience too high or too low

Using mode 'min' which stops when metric decreases

To stop training when validation AUC improves, monitor val_auc. Set patience to 5 to wait 5 epochs without improvement. Use mode max because higher AUC is better.

Practice

1. What does the ROC curve represent in machine learning?

easy

A. The number of layers in a neural network

B. The trade-off between true positive rate and false positive rate

C. The loss value during model training

D. The accuracy of the model on training data

Solution

1. Step 1: Understand ROC curve basics

   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 the trade-off between correctly identifying positives and incorrectly labeling negatives as positives.

3. Final Answer:

   The trade-off between true positive rate and false positive rate -> Option B

4. Quick Check:

   ROC curve = trade-off between TPR and FPR [OK]

Hint: ROC shows TPR vs FPR trade-off [OK]

Common Mistakes:

• Confusing ROC with accuracy
• Thinking ROC shows loss values
• Assuming ROC relates to model layers

2. Which TensorFlow function is used to compute the AUC metric during model training?

easy

A. tf.metrics.accuracy()

B. tf.keras.layers.Dense()

C. tf.losses.mean_squared_error()

D. tf.keras.metrics.AUC()

Solution

1. Step 1: Identify TensorFlow metric for AUC

   TensorFlow provides a built-in metric called tf.keras.metrics.AUC() to calculate the Area Under the ROC Curve.

2. Step 2: Differentiate from other functions

   tf.metrics.accuracy() measures accuracy, mean_squared_error() is a loss, and Dense() is a layer, not a metric.

3. Final Answer:

   tf.keras.metrics.AUC() -> Option D

4. Quick Check:

   AUC metric in TensorFlow = tf.keras.metrics.AUC() [OK]

Hint: AUC metric is tf.keras.metrics.AUC() [OK]

Common Mistakes:

• Using accuracy metric instead of AUC
• Confusing loss functions with metrics
• Selecting layer functions as metrics

3. Given the following TensorFlow code snippet, what will be the printed AUC value?

import tensorflow as tf

labels = [0, 0, 1, 1]
predictions = [0.1, 0.4, 0.35, 0.8]
auc_metric = tf.keras.metrics.AUC()
auc_metric.update_state(labels, predictions)
print(round(auc_metric.result().numpy(), 2))

medium

A. 0.75

B. 0.85

C. 0.65

D. 0.95

Solution

1. Step 1: Understand AUC calculation

   AUC measures how well predictions rank positive samples higher than negatives. Here, predictions are [0.1, 0.4, 0.35, 0.8] with labels [0,0,1,1].

2. Step 2: Calculate AUC manually

   Pairs: (0.35 > 0.1), (0.35 < 0.4), (0.8 > 0.1), (0.8 > 0.4). 3 out of 4 pairs rank correctly, so AUC ≈ 0.75.

3. Final Answer:

   0.75 -> Option A

4. Quick Check:

   Correct ranking pairs / total pairs = 0.75 [OK]

Hint: Count correct positive-negative pairs for AUC [OK]

Common Mistakes:

• Confusing AUC with accuracy
• Rounding errors in calculation
• Misordering prediction-label pairs

4. Identify the error in this TensorFlow code for computing ROC AUC:

import tensorflow as tf
labels = [0, 1, 0, 1]
predictions = [0.2, 0.8, 0.4, 0.6]
auc = tf.keras.metrics.AUC()
auc.update_state(predictions, labels)
print(auc.result().numpy())

medium

A. Labels and predictions are swapped in update_state

B. AUC metric is not initialized correctly

C. Labels contain invalid values

D. Missing model compilation step

Solution

1. Step 1: Check update_state argument order

   tf.keras.metrics.AUC().update_state() expects labels first, then predictions.

2. Step 2: Identify swapped arguments

   In the code, predictions are passed first and labels second, which is incorrect and causes wrong metric calculation.

3. Final Answer:

   Labels and predictions are swapped in update_state -> Option A

4. Quick Check:

   update_state(labels, predictions) order matters [OK]

Hint: Labels go first, predictions second in update_state [OK]

Common Mistakes:

• Swapping labels and predictions
• Ignoring argument order in functions
• Assuming metric auto-corrects input order

5. You trained a binary classifier in TensorFlow and want to improve the AUC score. Which approach is most effective?

hard

A. Use a smaller batch size only

B. Increase the number of epochs without changing data

C. Tune the classification threshold and retrain with balanced data

D. Remove the AUC metric from model evaluation

Solution

1. Step 1: Understand factors affecting AUC

   AUC depends on model's ability to rank positives higher than negatives, which improves with balanced data and proper threshold tuning.

2. Step 2: Evaluate options for improving AUC

   Increasing epochs alone may cause overfitting; smaller batch size alone may not improve ranking; removing AUC metric stops monitoring it.

3. Final Answer:

   Tune the classification threshold and retrain with balanced data -> Option C

4. Quick Check:

   Balanced data + threshold tuning improves AUC [OK]

Hint: Balance data and tune threshold to boost AUC [OK]

Common Mistakes:

• Ignoring data imbalance
• Overtraining without validation
• Removing important metrics