Precision-recall curves in TensorFlow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function that computes precision and recall.

TensorFlow

from sklearn.metrics import [1]

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

Aprecision_recall_curve

Broc_curve

Caccuracy_score

Dconfusion_matrix

Attempts:

3 left

💡 Hint

Common Mistakes

Importing roc_curve instead, which is for ROC curves.

Importing accuracy_score, which does not provide precision or recall values.

The precision_recall_curve function computes precision and recall values for different thresholds.

2fill in blank

medium

Complete the code to compute precision and recall from true labels and predicted scores.

TensorFlow

precision, recall, thresholds = precision_recall_curve(y_true, [1])

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

Ay_labels

By_pred

Cy_scores

Dy_prob

Attempts:

3 left

💡 Hint

Common Mistakes

Passing binary predictions instead of scores.

Passing true labels again instead of predicted scores.

The precision_recall_curve function requires the true labels and the predicted scores or probabilities, here named y_scores.

3fill in blank

hard

Fix the error in the code to plot the precision-recall curve correctly.

TensorFlow

plt.plot(recall, [1])
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('Precision-Recall Curve')
plt.show()

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

Ay_scores

Bthresholds

Cy_true

Dprecision

Attempts:

3 left

💡 Hint

Common Mistakes

Plotting thresholds instead of precision.

Plotting true labels or scores instead of precision.

Precision values should be plotted on the y-axis against recall on the x-axis.

4fill in blank

hard

Fill both blanks to compute average precision score and print it.

TensorFlow

from sklearn.metrics import [1]
avg_precision = [2](y_true, y_scores)
print(f'Average Precision: {avg_precision:.2f}')

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

Aaverage_precision_score

Bprecision_recall_curve

Droc_auc_score

Attempts:

3 left

💡 Hint

Common Mistakes

Importing roc_auc_score instead, which is for ROC AUC.

Using precision_recall_curve instead of average_precision_score.

The average_precision_score function computes the average precision from true labels and predicted scores.

5fill in blank

hard

Fill all three blanks to create a dictionary of precision and recall values for thresholds above 0.5.

TensorFlow

pr_dict = {threshold: ([1], [2]) for precision, recall, threshold in zip(precision, recall, [3]) if threshold > 0.5}

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

Aprecision

Brecall

Cthresholds

Dy_scores

Attempts:

3 left

💡 Hint

Common Mistakes

Using y_scores instead of thresholds in the zip.

Swapping precision and recall order in the tuple.

This dictionary comprehension pairs precision and recall values for thresholds greater than 0.5 using the thresholds array.

Practice

1. What does a precision-recall curve primarily show in model evaluation?

easy

A. The loss value during training

B. The trade-off between precision and recall for different thresholds

C. The accuracy of the model on the test set

D. The number of false negatives only

Solution

1. Step 1: Understand precision and recall

   Precision measures correct positive predictions, recall measures how many actual positives are found.

2. Step 2: Understand what the curve shows

   The precision-recall curve plots precision against recall at different classification thresholds, showing their trade-off.

3. Final Answer:

   The trade-off between precision and recall for different thresholds -> Option B

4. Quick Check:

   Precision-recall curve = trade-off between precision and recall [OK]

Hint: Precision-recall curves show precision vs recall trade-off [OK]

Common Mistakes:

• Confusing precision-recall curve with accuracy plot
• Thinking it shows loss values
• Assuming it only shows false negatives

2. Which TensorFlow function is used to compute precision and recall values for plotting a precision-recall curve?

easy

A. tf.keras.metrics.Precision() and tf.keras.metrics.Recall()

B. tf.keras.losses.MeanSquaredError()

C. tf.data.Dataset.batch()

D. tf.random.uniform()

Solution

1. Step 1: Identify functions related to precision and recall

   TensorFlow provides tf.keras.metrics.Precision() and tf.keras.metrics.Recall() to compute these metrics.

2. Step 2: Exclude unrelated functions

   MeanSquaredError is a loss, Dataset.batch is for data batching, random.uniform generates random numbers.

3. Final Answer:

   tf.keras.metrics.Precision() and tf.keras.metrics.Recall() -> Option A

4. Quick Check:

   Precision and recall functions = tf.keras.metrics.Precision and tf.keras.metrics.Recall [OK]

Hint: Use tf.keras.metrics.Precision and tf.keras.metrics.Recall for these metrics [OK]

Common Mistakes:

• Using loss functions instead of metric functions
• Confusing data pipeline functions with metrics
• Using random functions for metrics

3. Given the following TensorFlow code snippet, what will be the shape of the precision and recall tensors?

import tensorflow as tf
from sklearn.metrics import precision_recall_curve
y_true = tf.constant([0, 1, 1, 0, 1])
y_scores = tf.constant([0.1, 0.4, 0.35, 0.8, 0.7])
precision, recall, thresholds = precision_recall_curve(y_true, y_scores)
print(precision.shape, recall.shape)

medium

A. (1,) (1,)

B. (5,) (5,)

C. (N,) (N,) where N is number of thresholds + 1

D. Scalar values

Solution

1. Step 1: Understand precision_recall_curve output

   The function returns precision and recall arrays for each threshold plus one extra point, so length is number of thresholds + 1.

2. Step 2: Analyze shapes

   Since thresholds vary, precision and recall are vectors, not scalars or fixed length equal to input size.

3. Final Answer:

   (N,) (N,) where N is number of thresholds + 1 -> Option C

4. Quick Check:

   Precision and recall shapes = vectors with length thresholds+1 [OK]

Hint: precision_recall_curve returns arrays, not scalars [OK]

Common Mistakes:

• Assuming output shapes match input size
• Expecting scalar precision and recall
• Confusing thresholds count with input length

4. The following code attempts to plot a precision-recall curve but raises an error. What is the likely cause?

import tensorflow as tf
import matplotlib.pyplot as plt

y_true = tf.constant([0, 1, 1, 0])
y_scores = tf.constant([0.2, 0.8, 0.6, 0.4])
precision, recall, thresholds = tf.metrics.PrecisionRecallCurve()(y_true, y_scores)
plt.plot(precision, recall)
plt.show()

medium

A. Precision and recall are swapped in the plot call

B. plt.plot requires thresholds as x-axis

C. y_true and y_scores must be numpy arrays, not tensors

D. tf.metrics.PrecisionRecallCurve() does not exist

Solution

1. Step 1: Check TensorFlow API correctness

   TensorFlow does not have tf.metrics.PrecisionRecallCurve(); typically use from sklearn.metrics import precision_recall_curve.

2. Step 2: Understand error cause

   Using a non-existent function causes an AttributeError before plotting.

3. Final Answer:

   tf.metrics.PrecisionRecallCurve() does not exist -> Option D

4. Quick Check:

   Correct approach is sklearn.metrics.precision_recall_curve [OK]

Hint: Use sklearn.metrics.precision_recall_curve, not tf.metrics [OK]

Common Mistakes:

• Using non-existent TensorFlow function for PR curve
• Swapping precision and recall axes
• Assuming tensors can't be plotted

5. You have a highly imbalanced dataset with 1% positive class. You want to evaluate your TensorFlow model's ability to detect positives. Which approach is best to use and why? A) Use accuracy metric only B) Use confusion matrix without thresholds C) Use loss value during training D) Use precision-recall curve to evaluate model

hard

A. Use precision-recall curve to evaluate model

B. Use confusion matrix without thresholds

C. Use loss value during training

D. Use accuracy metric only

Solution

1. Step 1: Understand dataset imbalance impact

   With 1% positives, accuracy can be misleading because predicting all negatives gives high accuracy.

2. Step 2: Choose appropriate evaluation metric

   Precision-recall curves focus on positive class detection and show trade-offs, ideal for imbalanced data.

3. Final Answer:

   Use precision-recall curve to evaluate model -> Option A

4. Quick Check:

   Imbalanced data = use precision-recall curve [OK]

Hint: For imbalanced data, prefer precision-recall curves [OK]

Common Mistakes:

• Relying on accuracy for imbalanced data
• Ignoring threshold effects in confusion matrix
• Using loss value as evaluation metric