Evaluation and confusion matrix in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function that computes the confusion matrix.

Computer Vision

from sklearn.metrics import [1]

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

Atrain_test_split

Bconfusion_matrix

Cclassification_report

Daccuracy_score

Attempts:

3 left

💡 Hint

Common Mistakes

Importing accuracy_score instead of confusion_matrix

Importing train_test_split which is for splitting data

The confusion_matrix function from sklearn.metrics is used to compute the confusion matrix for classification results.

2fill in blank

medium

Complete the code to calculate accuracy from true and predicted labels.

Computer Vision

accuracy = sum(y_true == y_pred) / [1]

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

Alen(y_pred)

Bsum(y_pred)

Clen(y_true)

Dsum(y_true)

Attempts:

3 left

💡 Hint

Common Mistakes

Dividing by sum of predictions instead of total samples

Using length of predictions which might differ if data is inconsistent

Accuracy is the number of correct predictions divided by the total number of true labels, which is len(y_true).

3fill in blank

hard

Fix the error in the code to correctly compute the confusion matrix.

Computer Vision

cm = confusion_matrix([1], y_pred)

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Apredictions

By_pred

Clabels

Dy_true

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping true and predicted labels

Passing undefined variable names

The first argument to confusion_matrix must be the true labels y_true, followed by predicted labels.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that counts true positives for each class.

Computer Vision

true_positives = {cls: sum((y_true == cls) & ([1] == cls)) for cls in [2]

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Ay_pred

By_true

Cclasses

Dlabels

Attempts:

3 left

💡 Hint

Common Mistakes

Using y_true instead of y_pred in the condition

Using undefined variable names for classes

We compare predicted labels y_pred to each class and iterate over all classes stored in classes to count true positives.

5fill in blank

hard

Fill all three blanks to compute precision for each class from the confusion matrix.

Computer Vision

precision = {j: cm[[1], [2]] / sum(cm[:, [3]]) for j in classes}

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Ai

Bj

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing row and column indices

Dividing by sum over rows instead of columns

Precision for each class is true positives cm[j, j] divided by the sum of predicted positives sum(cm[:, j]). Here, j represents the class index.

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