Confusion matrix in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function that creates a confusion matrix.

ML Python

from sklearn.metrics import [1]

# Now you can use confusion_matrix() to evaluate predictions

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

Aclassification_report

Bconfusion_matrix

Caccuracy_score

Droc_auc_score

Attempts:

3 left

💡 Hint

Common Mistakes

Importing accuracy_score instead of confusion_matrix

Using classification_report which is for detailed metrics, not the matrix itself

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

2fill in blank

medium

Complete the code to compute the confusion matrix for true and predicted labels.

ML Python

y_true = [0, 1, 0, 1]
y_pred = [0, 0, 0, 1]
cm = [1](y_true, y_pred)
print(cm)

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

Aaccuracy_score

Bprecision_score

Croc_auc_score

Dconfusion_matrix

Attempts:

3 left

💡 Hint

Common Mistakes

Using accuracy_score which returns a single number, not a matrix

Using precision_score which returns a metric, not the matrix

The confusion_matrix function takes true labels and predicted labels to compute the matrix.

3fill in blank

hard

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

ML Python

from sklearn.metrics import confusion_matrix

y_true = [1, 0, 1, 0]
y_pred = [1, 1, 1, 0]
cm = confusion_matrix(y_true, [1])
print(cm)

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

Aconfusion_matrix

By_true

Cy_pred

Daccuracy_score

Attempts:

3 left

💡 Hint

Common Mistakes

Passing true labels twice

Passing a metric function instead of predicted labels

The second argument to confusion_matrix must be the predicted labels, y_pred.

4fill in blank

hard

Fill all three blanks to create a confusion matrix and extract true positives and false negatives.

ML Python

cm = [1](y_true, y_pred)
true_positives = cm[[2]][[2]]
false_negatives = cm[[2]][[3]]

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

Aconfusion_matrix

B0

C1

Daccuracy_score

Attempts:

3 left

💡 Hint

Common Mistakes

Using accuracy_score instead of confusion_matrix

Mixing up row and column indices for true positives

Use confusion_matrix to get the matrix. For binary classification, true positives are at row 1, column 1 (index 1,1).

False negatives are at row 1, column 0 (index 1,0).

5fill in blank

hard

Fill all three blanks to compute the confusion matrix and calculate accuracy manually.

ML Python

cm = [1](y_true, y_pred)
total = cm[[2]][[2]] + cm[[3]][[3]] + cm[[2]][[3]] + cm[[3]][[2]]
accuracy = (cm[[2]][[2]] + cm[[3]][[3]]) / total
print(f"Accuracy: {accuracy:.2f}")

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

Aaccuracy_score

Bconfusion_matrix

C0

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using accuracy_score function instead of manual calculation

Mixing up class indices in the matrix

Use confusion_matrix to get the matrix.

Indices 0 and 1 represent the two classes in binary classification.

Accuracy is the sum of true positives and true negatives divided by total samples.

Practice

1.

What does a confusion matrix primarily show in machine learning?

easy

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

B. The speed of the model training process

C. The size of the dataset used for training

D. The number of features in the dataset

Solution

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

   A confusion matrix shows how many times a model predicted each class correctly or incorrectly.

2. Step 2: Identify what it displays

   It displays counts of true positives, true negatives, false positives, and false negatives for each class.

3. Final Answer:

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

4. Quick Check:

   Confusion matrix = correct and incorrect counts [OK]

Hint: Confusion matrix counts correct and wrong guesses per class [OK]

Common Mistakes:

• Thinking it shows training speed
• Confusing it with dataset size
• Assuming it shows feature count

2.

Which of the following is the correct way to represent a confusion matrix for a binary classifier?

[[TP, FP],
 [FN, TN]]

easy

A. true Positives, false Positives in first row; false Negatives, true Negatives in second row

B. true Negatives, false Negatives in first row; false Positives, true Positives in second row

C. false Positives, true Positives in first row; true Negatives, false Negatives in second row

D. false Negatives, true Negatives in first row; true Positives, false Positives in second row

Solution

1. Step 1: Recall confusion matrix layout for binary classification

   The standard layout is: first row has true Positives (TP) and false Positives (FP), second row has false Negatives (FN) and true Negatives (TN).

2. Step 2: Match the given matrix to the layout

   The given matrix [[TP, FP], [FN, TN]] matches the standard layout exactly.

3. Final Answer:

   true Positives, false Positives in first row; false Negatives, true Negatives in second row -> Option A

4. Quick Check:

   Binary confusion matrix = [[TP, FP], [FN, TN]] [OK]

Hint: Remember TP and FP are in the first row [OK]

Common Mistakes:

• Swapping rows and columns
• Confusing FP with FN positions
• Mixing up TP and TN placement

3.

Given the confusion matrix for a 3-class classifier:

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

What is the total number of correct predictions?

medium

A. 22

B. 20

C. 21

D. 18

Solution

1. Step 1: Identify correct predictions in confusion matrix

   Correct predictions are on the diagonal: 5 (class 1), 7 (class 2), and 6 (class 3).

2. Step 2: Sum the diagonal values

   5 + 7 + 6 = 18 correct predictions in total.

3. Final Answer:

   18 -> Option D

4. Quick Check:

   Sum diagonal = 18 correct predictions [OK]

Hint: Sum diagonal elements for correct predictions [OK]

Common Mistakes:

• Summing all matrix elements instead of diagonal
• Adding off-diagonal values by mistake
• Ignoring one of the diagonal values

4.

Consider this confusion matrix for a binary classifier:

[[50, 10],
 [5, 35]]

There is a mistake in interpreting the false Positives (FP). Which is the correct FP count?

medium

A. 50

B. 5

C. 10

D. 35

Solution

1. Step 1: Recall FP position in confusion matrix

   In a binary confusion matrix [[TP, FP], [FN, TN]], FP is at position [0][1].

2. Step 2: Identify FP value in given matrix

   Value at [0][1] is 10, so FP = 10.

3. Final Answer:

   10 -> Option C

4. Quick Check:

   FP = matrix[0][1] = 10 [OK]

Hint: FP is always top-right in binary confusion matrix [OK]

Common Mistakes:

• Confusing FP with FN
• Taking FP as bottom-left value
• Mixing FP with TN or TP

5.

A model's confusion matrix for a 3-class problem is:

[[40, 5, 0],
 [3, 30, 7],
 [0, 2, 38]]

Calculate the overall accuracy of the model.

hard

A. 0.82

B. 0.85

C. 0.88

D. 0.90

Solution

1. Step 1: Calculate total correct predictions

   Sum diagonal: 40 + 30 + 38 = 108 correct predictions.

2. Step 2: Calculate total predictions

   Sum all elements: (40+5+0) + (3+30+7) + (0+2+38) = 45 + 40 + 40 = 125 total predictions.

3. Step 3: Compute accuracy

   Accuracy = correct / total = 108 / 125 = 0.864, rounded to 0.86.

4. Final Answer:

   0.86 -> Option B

5. Quick Check:

   Accuracy = 108/125 ≈ 0.86 [OK]

Hint: Accuracy = sum diagonal / sum all elements [OK]

Common Mistakes:

• Using only one class's correct count
• Forgetting to sum all matrix elements
• Rounding incorrectly