Confusion matrix analysis in TensorFlow - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Confusion matrix shape from TensorFlow predictions

Given a binary classification model in TensorFlow, what is the shape of the confusion matrix returned by tf.math.confusion_matrix when comparing true labels and predicted labels?

TensorFlow

import tensorflow as tf
true_labels = tf.constant([0, 1, 0, 1, 1])
pred_labels = tf.constant([0, 0, 0, 1, 1])
cm = tf.math.confusion_matrix(true_labels, pred_labels)
cm_shape = cm.shape
print(cm_shape)

A (1, 2)

B (5, 5)

C (3, 3)

D (2, 2)

💡 Hint

The confusion matrix size depends on the number of classes, not the number of samples.

For binary classification, the confusion matrix is 2x2 because there are two classes: 0 and 1.

❓ Metrics

intermediate

Calculating accuracy from confusion matrix

Given the confusion matrix below, what is the accuracy of the model? [[50, 10], [5, 35]]

A 0.80

B 0.85

C 0.90

D 0.75

💡 Hint

Accuracy = (True Positives + True Negatives) / Total samples

Accuracy = (50 + 35) / (50 + 10 + 5 + 35) = 85 / 100 = 0.85

❓ Model Choice

advanced

Choosing model type based on confusion matrix imbalance

You have a confusion matrix with many false negatives but few false positives. Which model adjustment is best to reduce false negatives?

A Increase recall by adjusting the classification threshold lower

B Increase precision by adjusting the classification threshold higher

C Use a simpler model to reduce overfitting

D Use early stopping during training

💡 Hint

Recall focuses on reducing false negatives.

Lowering the classification threshold increases recall, reducing false negatives.

🔧 Debug

advanced

Identifying error in confusion matrix calculation code

What error will this TensorFlow code raise? import tensorflow as tf true = tf.constant([0, 1, 2]) pred = tf.constant([0, 1]) cm = tf.math.confusion_matrix(true, pred) print(cm)

TensorFlow

import tensorflow as tf
true = tf.constant([0, 1, 2])
pred = tf.constant([0, 1])
cm = tf.math.confusion_matrix(true, pred)
print(cm)

A ValueError due to invalid label values

B TypeError due to wrong input types

C InvalidArgumentError due to shape mismatch

D No error, prints confusion matrix

💡 Hint

True and predicted labels must have the same length.

The true and predicted label tensors have different lengths, causing an InvalidArgumentError.

🧠 Conceptual

expert

Interpreting confusion matrix for multi-class classification

In a 3-class classification problem, the confusion matrix is: [[30, 2, 3], [4, 25, 1], [5, 0, 35]] Which class has the highest precision?

A Class 1

B Class 2

C Class 0

D All classes have equal precision

💡 Hint

Precision = True Positives / (True Positives + False Positives) for each class.

Class 1 precision = 25 / (2 + 25 + 0) = 25 / 27 ≈ 0.926, which is higher than others.

Practice

1. What does a confusion matrix primarily show in a classification problem?

easy

A. The number of correct and incorrect predictions for each class

B. The training time of the model

C. The number of features used in the model

D. The learning rate of the optimizer

Solution

1. Step 1: Understand the purpose of a confusion matrix

   A confusion matrix displays how many predictions were correct or wrong for each class in classification.

2. Step 2: Compare options with the definition

   Only The number of correct and incorrect predictions for each class correctly describes this purpose; others relate to unrelated model aspects.

3. Final Answer:

   The number of correct and incorrect predictions for each class -> Option A

4. Quick Check:

   Confusion matrix = correct/wrong counts [OK]

Hint: Confusion matrix counts correct vs wrong predictions per class [OK]

Common Mistakes:

• Confusing confusion matrix with training metrics like loss
• Thinking it shows model parameters
• Assuming it shows training time

2. Which TensorFlow function is used to compute a confusion matrix from true labels and predicted labels?

easy

A. tf.data.Dataset.from_tensor_slices()

B. tf.keras.metrics.Accuracy()

C. tf.math.confusion_matrix()

D. tf.nn.softmax()

Solution

1. Step 1: Identify TensorFlow functions related to confusion matrix

   The function tf.math.confusion_matrix() directly computes the confusion matrix from labels.

2. Step 2: Eliminate unrelated functions

   tf.keras.metrics.Accuracy() measures accuracy, tf.data.Dataset.from_tensor_slices() creates datasets, and tf.nn.softmax() applies softmax activation.

3. Final Answer:

   tf.math.confusion_matrix() -> Option C

4. Quick Check:

   Confusion matrix function = tf.math.confusion_matrix() [OK]

Hint: Use tf.math.confusion_matrix() for confusion matrix [OK]

Common Mistakes:

• Using accuracy metric instead of confusion matrix function
• Confusing dataset creation with confusion matrix
• Mixing activation functions with metrics

3. Given the following code, what is the output confusion matrix?

import tensorflow as tf
true_labels = [0, 1, 2, 2, 0]
pred_labels = [0, 2, 2, 2, 0]
cm = tf.math.confusion_matrix(true_labels, pred_labels)
print(cm.numpy())

medium

A. [[2 0 0]
[0 0 0]
[0 1 2]]

B. [[2 0 0]
[0 1 0]
[0 0 2]]

C. [[1 0 1]
[0 0 1]
[0 0 2]]

D. [[2 0 0]
[0 0 1]
[0 0 2]]

Solution

1. Step 1: Count true vs predicted labels for each class

   Class 0 true: predicted 0 twice; class 1 true: predicted 2 once; class 2 true: predicted 2 twice.

2. Step 2: Build confusion matrix rows

   Row 0: [2 correct (0 predicted as 0), 0 predicted as 1, 0 predicted as 2]; Row 1: [0, 0, 1]; Row 2: [0, 0, 2].

3. Final Answer:

   [[2 0 0] [0 0 1] [0 0 2]] -> Option D

4. Quick Check:

   Count matches matrix entries [OK]

Hint: Count true vs predicted labels per class to fill matrix [OK]

Common Mistakes:

• Mixing up rows and columns in the matrix
• Counting predicted labels instead of true labels
• Forgetting to count all instances

4. Identify the error in this TensorFlow code snippet for confusion matrix calculation:

import tensorflow as tf
true = [0, 1, 1, 0]
pred = [0, 1, 0]
cm = tf.math.confusion_matrix(true, pred)
print(cm)

medium

A. The confusion matrix function is misspelled

B. The true and predicted label lists have different lengths

C. TensorFlow is not imported correctly

D. The print statement is missing parentheses

Solution

1. Step 1: Check input list lengths

   True labels list has 4 elements; predicted labels list has 3 elements, causing mismatch.

2. Step 2: Understand function requirements

   tf.math.confusion_matrix() requires true and predicted labels to be same length to compare element-wise.

3. Final Answer:

   The true and predicted label lists have different lengths -> Option B

4. Quick Check:

   Input lengths must match [OK]

Hint: Ensure true and predicted label lists have same length [OK]

Common Mistakes:

• Ignoring length mismatch error
• Assuming function name typo
• Thinking print syntax is wrong in Python 3

5. You have a 3-class classification problem with classes 0, 1, and 2. After training, your confusion matrix is:

[[50  2  3]
 [ 4 45  1]
 [ 5  2 40]]

Which class has the highest precision, and how do you calculate it from the matrix?

hard

A. Class 1; precision = TP / (TP + FP) = 45 / (45 + 4 + 1)

B. Class 2; precision = TP / (TP + FP) = 40 / (3 + 1 + 40)

C. Class 0; precision = TP / (TP + FP) = 50 / (50 + 4 + 5)

D. Class 1; precision = TP / (TP + FP) = 45 / (2 + 45 + 2)

Solution

1. Step 1: Understand precision calculation

   Precision for a class = True Positives (TP) / (TP + False Positives (FP)). TP is diagonal element; FP is sum of column excluding TP.

2. Step 2: Calculate precision for each class

   Class 0: TP=50; FP=sum column 0 excluding TP=4+5=9; precision=50/(50+9)=50/59≈0.847. Class 1: TP=45; FP=2+2=4; precision=45/(45+4)=45/49≈0.918. Class 2: TP=40; FP=3+1=4; precision=40/(40+4)=40/44≈0.909.

3. Step 3: Identify highest precision

   Class 1 has the highest precision (≈0.918).

4. Final Answer:

   Class 1; precision = TP / (TP + FP) = 45 / (45 + 4 + 1) -> Option A

5. Quick Check:

   Precision = TP / (TP + FP) [OK]

Hint: Precision = TP / (TP + FP) using confusion matrix columns [OK]

Common Mistakes:

• Confusing precision with recall
• Using row sums instead of column sums for FP
• Mixing up TP and FP positions