Evaluation and confusion matrix in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of confusion matrix calculation

What is the output of the following Python code that computes a confusion matrix for a binary classification?

Computer Vision

from sklearn.metrics import confusion_matrix
true_labels = [0, 1, 0, 1, 0, 1, 1]
pred_labels = [0, 0, 0, 1, 0, 1, 1]
cm = confusion_matrix(true_labels, pred_labels)
print(cm)

A

[[3 0]
 [1 3]]

B

[[2 1]
 [1 3]]

C

[[3 1]
 [0 3]]

D

[[3 0]
 [0 4]]

💡 Hint

Recall confusion matrix rows represent true classes, columns predicted classes.

The confusion matrix counts true negatives (3), false positives (0), false negatives (1), and true positives (3).

🧠 Conceptual

intermediate

Understanding precision and recall from confusion matrix

Given a confusion matrix for a binary classifier: [[50, 10], [5, 35]], what is the recall value?

A 0.778

B 0.875

C 0.833

D 0.909

💡 Hint

Recall = True Positives / (True Positives + False Negatives)

Recall = 35 / (35 + 5) = 35 / 40 = 0.875

❓ Metrics

advanced

Choosing the best metric for imbalanced data

Which metric is most appropriate to evaluate a model on a highly imbalanced dataset where the positive class is rare?

A F1-score

B Accuracy

C Recall

D Precision

💡 Hint

Consider a metric that balances precision and recall.

F1-score balances precision and recall, making it suitable for imbalanced classes.

🔧 Debug

advanced

Identify the error in confusion matrix code

What error will this code raise? from sklearn.metrics import confusion_matrix true = [1, 0, 1] pred = [0, 1] cm = confusion_matrix(true, pred) print(cm)

Computer Vision

from sklearn.metrics import confusion_matrix
true = [1, 0, 1]
pred = [0, 1]
cm = confusion_matrix(true, pred)
print(cm)

A No error, prints confusion matrix

B TypeError: unsupported operand type(s) for +: 'int' and 'str'

C IndexError: list index out of range

D ValueError: Found input variables with inconsistent numbers of samples

💡 Hint

Check if true and pred lists have the same length.

The true and predicted label lists have different lengths, causing ValueError.

❓ Model Choice

expert

Best model choice based on confusion matrix analysis

You have two models evaluated on the same test set with confusion matrices: Model A: [[90, 10], [30, 70]] Model B: [[80, 20], [10, 90]] Which model has better recall for the positive class?

A Model A has better recall

B Both have the same recall

C Model B has better recall

D Recall cannot be determined from confusion matrix

💡 Hint

Recall = TP / (TP + FN)

Model A recall = 70 / (70 + 30) = 0.7; Model B recall = 90 / (90 + 10) = 0.9; Model B has better recall.

Practice

1. What does a confusion matrix help you understand in a classification model?

easy

A. The speed of the model during training

B. How well the model predicts each class by showing true and false predictions

C. The number of layers in the model

D. The size of the input images

Solution

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

   A confusion matrix shows counts of correct and incorrect predictions for each class, helping evaluate classification performance.

2. Step 2: Match the description to the options

   Only How well the model predicts each class by showing true and false predictions describes this purpose correctly, while others relate to unrelated model aspects.

3. Final Answer:

   How well the model predicts each class by showing true and false predictions -> Option B

4. Quick Check:

   Confusion matrix = True/False predictions summary [OK]

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

Common Mistakes:

• Confusing confusion matrix with model speed
• Thinking it shows model architecture details
• Assuming it shows input data size

2. Which of the following is the correct way to create a confusion matrix using scikit-learn in Python?

easy

A. confusion_matrix(y_pred)

B. confusionMatrix(y_true, y_pred)

C. conf_matrix(y_pred, y_true)

D. confusion_matrix(y_true, y_pred)

Solution

1. Step 1: Recall the scikit-learn function signature

   The function to create a confusion matrix is confusion_matrix(y_true, y_pred) with true labels first, then predicted labels.

2. Step 2: Check each option for correctness

   confusion_matrix(y_true, y_pred) matches the correct function and argument order. Options B, C, and D have wrong names or argument orders.

3. Final Answer:

   confusion_matrix(y_true, y_pred) -> Option D

4. Quick Check:

   Correct function name and argument order [OK]

Hint: Use exact function name and order: confusion_matrix(true, pred) [OK]

Common Mistakes:

• Using wrong function name capitalization
• Swapping true and predicted labels
• Passing only one argument

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

from sklearn.metrics import confusion_matrix

y_true = [0, 1, 0, 1, 0, 1, 1]
y_pred = [0, 0, 0, 1, 0, 1, 1]

cm = confusion_matrix(y_true, y_pred)
print(cm)

medium

A. [[3 0]
[1 3]]

B. [[2 1]
[0 4]]

C. [[3 1]
[0 3]]

D. [[4 0]
[1 2]]

Solution

1. Step 1: Count true positives and negatives

   Class 0 true positives: y_true=0 and y_pred=0 occur 3 times; false negatives: y_true=1 but y_pred=0 occur once.

2. Step 2: Build confusion matrix

   Matrix rows = true labels, columns = predicted labels. So cm = [[3,0],[1,3]] matches counts.

3. Final Answer:

   [[3 0] [1 3]] -> Option A

4. Quick Check:

   Count matches matrix entries [OK]

Hint: Count true/pred pairs carefully to fill matrix [OK]

Common Mistakes:

• Mixing rows and columns order
• Counting predicted labels as true labels
• Ignoring zero counts

4. You wrote this code but got an error:

from sklearn.metrics import confusion_matrix

cm = confusion_matrix(y_pred, y_true)
print(cm)

What is the likely cause of the error?

medium

A. Using print instead of return

B. Missing import statement for confusion_matrix

C. Swapped y_pred and y_true arguments causing shape mismatch

D. y_pred and y_true are not defined variables

Solution

1. Step 1: Check argument order for confusion_matrix

   The function expects y_true first, then y_pred. Swapping them can cause errors or wrong results.

2. Step 2: Analyze the error cause

   Since import is present and print is valid, the likely cause is swapped arguments causing shape or value errors.

3. Final Answer:

   Swapped y_pred and y_true arguments causing shape mismatch -> Option C

4. Quick Check:

   Correct argument order is true labels first [OK]

Hint: Always pass true labels first, predicted second [OK]

Common Mistakes:

• Swapping true and predicted labels
• Forgetting to import confusion_matrix
• Using undefined variables

5. You have a 3-class image classifier with classes A, B, and C. The confusion matrix is:

[[5 2 0]
 [1 7 1]
 [0 2 6]]

What is the precision for class B?

hard

A. 7 / (2 + 7 + 2) = 0.58

B. 7 / (1 + 7 + 1) = 0.7

C. 7 / (5 + 1 + 0) = 0.7

D. 7 / (7 + 1 + 2) = 0.58

Solution

1. Step 1: Identify precision formula for class B

   Precision = True Positives for B / (All predicted as B). True Positives = cm[1][1] = 7.

2. Step 2: Calculate total predicted as B

   Sum column 1: cm[0][1]=2 + cm[1][1]=7 + cm[2][1]=2 = 11. So precision = 7/11 ≈ 0.636, closest to 0.58 in 7 / (2 + 7 + 2) = 0.58.

3. Final Answer:

   7 / (2 + 7 + 2) = 0.58 -> Option A

4. Quick Check:

   Precision = TP / predicted positives [OK]

Hint: Precision = TP / sum of predicted class column [OK]

Common Mistakes:

• Using row sums instead of column sums
• Confusing precision with recall
• Ignoring off-diagonal values in predicted class column