Error rate and failure analysis in Agentic Ai - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 conceptual

intermediate

Understanding Error Rate in Classification

Which of the following best describes the error rate in a classification model?

A The proportion of all predictions that are incorrect out of the total predictions made.

B The proportion of correct predictions out of the total predictions made.

C The number of true positives divided by false negatives.

D The ratio of false positives to true negatives.

❓ metrics

intermediate

Calculating Failure Rate from Confusion Matrix

Given a confusion matrix for a binary classifier:
True Positives = 80, True Negatives = 90, False Positives = 10, False Negatives = 20.
What is the failure rate (error rate) of this model?

A 0.10

B 0.20

C 0.15

D 0.25

🔧 debug

advanced

Identifying the Cause of High Error Rate

A model trained on a dataset shows a high error rate on the test set but low error rate on the training set. What is the most likely cause?

A The model is underfitting and needs more training epochs.

B The model is overfitting the training data and failing to generalize.

C The test set is too small to measure error rate accurately.

D The training data contains too many missing values.

❓ model choice

advanced

Choosing a Model to Reduce Failure Rate on Imbalanced Data

You have a dataset with 95% of one class and 5% of another. Which model or technique is best to reduce failure rate on the minority class?

A Use a decision tree with balanced class weights or oversampling of minority class.

B Use a clustering algorithm like K-means.

C Use a linear regression model.

D Use a simple logistic regression without class weighting.

💻 code output

expert

Output of Failure Rate Calculation Code

What is the output of this Python code that calculates failure rate?

Agentic_ai

def failure_rate(conf_matrix):
    TP, TN, FP, FN = conf_matrix
    total = TP + TN + FP + FN
    return (FP + FN) / total

conf_matrix = (50, 40, 5, 5)
print(failure_rate(conf_matrix))

A 0.05

B 0.15

C 0.20

D 0.10