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.