Confusion matrix in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Confusion Matrix Components

Which part of the confusion matrix represents the number of times the model correctly predicted the positive class?

A False Positive (FP)

B True Positive (TP)

C True Negative (TN)

D False Negative (FN)

💡 Hint

Think about when the model predicts positive and the actual label is also positive.

True Positive (TP) counts the cases where the model correctly predicts the positive class.

❓ Predict Output

intermediate

Output of Confusion Matrix Calculation

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

ML Python

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

A [[2, 1], [1, 3]]

B [[3, 0], [2, 2]]

C [[2, 0], [2, 3]]

D [[1, 2], [1, 3]]

💡 Hint

Count how many times each true label matches predicted label and mismatches.

The confusion matrix rows represent true labels and columns predicted labels. Here, [[2,1],[1,3]] means 2 true negatives, 1 false positive, 1 false negative, and 3 true positives.

❓ Metrics

advanced

Calculating Accuracy from Confusion Matrix

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

A 0.85

B 0.75

C 0.80

D 0.90

💡 Hint

Accuracy = (TP + TN) / Total samples.

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

🔧 Debug

advanced

Identifying Error in Confusion Matrix Calculation

What error will the following code raise?

ML Python

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

A IndexError: list index out of range

B TypeError: 'int' object is not iterable

C ValueError: Found input variables with inconsistent numbers of samples

D No error, prints confusion matrix

💡 Hint

Check if true and predicted label lists have the same length.

The true and predicted label lists have different lengths, causing a ValueError about inconsistent sample numbers.

❓ Model Choice

expert

Choosing the Best Metric for Imbalanced Data

You have a dataset with 95% negative and 5% positive samples. Which confusion matrix derived metric is best to evaluate your model's ability to detect positives?

A Specificity

B Precision

C Accuracy

D Recall (Sensitivity)

💡 Hint

Think about which metric focuses on catching positive cases even if they are rare.

Recall measures how many actual positives the model correctly identifies, which is crucial for imbalanced data where positives are rare.

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