Precision-recall curves in TensorFlow

Introduction

Precision-recall curves help us see how well a model finds positive cases without too many mistakes. They show the balance between catching true positives and avoiding false alarms.

When you want to check how good a model is at finding rare events, like fraud detection.

When false positives are costly, such as in medical diagnosis.

When you want to compare models on how well they identify positive cases.

When accuracy is misleading because classes are imbalanced.

When tuning a model to find the best trade-off between precision and recall.

Syntax

TensorFlow

from sklearn.metrics import precision_recall_curve

precision, recall, thresholds = precision_recall_curve(y_true, y_scores)

y_true is the list of true labels (0 or 1).

y_scores are the predicted probabilities or scores for the positive class.

Examples

Calculate precision and recall for a small example with 4 samples.

TensorFlow

precision, recall, thresholds = precision_recall_curve([0, 1, 1, 0], [0.1, 0.9, 0.8, 0.3])

Plot the precision-recall curve to visualize the trade-off.

TensorFlow

import matplotlib.pyplot as plt
plt.plot(recall, precision)
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('Precision-Recall Curve')
plt.show()

Sample Model

This code calculates and prints precision and recall values for different thresholds, then plots the precision-recall curve.

TensorFlow

import tensorflow as tf
from sklearn.metrics import precision_recall_curve
import matplotlib.pyplot as plt

# True labels (0 = negative, 1 = positive)
y_true = [0, 0, 1, 1]

# Model predicted probabilities for positive class
y_scores = [0.1, 0.4, 0.35, 0.8]

# Calculate precision, recall, thresholds
precision, recall, thresholds = precision_recall_curve(y_true, y_scores)

# Print the values
print('Precision:', precision)
print('Recall:', recall)
print('Thresholds:', thresholds)

# Plot the precision-recall curve
plt.plot(recall, precision, marker='.')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('Precision-Recall Curve')
plt.grid(True)
plt.show()

Important Notes

Precision-recall curves are especially useful when the positive class is rare.

The curve shows how precision and recall change as you change the decision threshold.

Higher area under the precision-recall curve means better model performance.

Summary

Precision-recall curves help evaluate models on positive class detection.

They show the trade-off between precision (correct positive predictions) and recall (found positives).

Use them when classes are imbalanced or false positives matter.

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