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)

❓ 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]]

❓ 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

🔧 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

❓ 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)