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.

💡 Hint

Think about what 'error rate' means in everyday terms: how often does the model get it wrong?

Error rate measures how often the model makes wrong predictions compared to all predictions. It is the complement of accuracy.

❓ 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

💡 Hint

Failure rate = (False Positives + False Negatives) / Total predictions.

Total predictions = 80 + 90 + 10 + 20 = 200.
Failures = 10 + 20 = 30.
Failure rate = 30 / 200 = 0.15.

🔧 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.

💡 Hint

Think about what it means when training error is low but test error is high.

Overfitting means the model learned the training data too well, including noise, so it performs poorly on new data.

❓ 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.

💡 Hint

Think about how to handle imbalanced classes to reduce errors on the minority group.

Balanced class weights or oversampling help the model pay more attention to the minority class, reducing its error rate.

❓ Predict 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

💡 Hint

Calculate (FP + FN) / total using the given numbers.

FP + FN = 5 + 5 = 10, total = 50 + 40 + 5 + 5 = 100, so failure rate = 10 / 100 = 0.10.

Practice

1. What does the error rate in a machine learning model represent?

easy

A. The percentage of wrong predictions made by the model

B. The time taken to train the model

C. The number of features used in the model

D. The size of the training dataset

Solution

1. Step 1: Understand what error rate measures

   Error rate measures how often the model's predictions are incorrect compared to the true answers.

2. Step 2: Relate error rate to model performance

   A higher error rate means more wrong predictions, so it shows the model's mistakes.

3. Final Answer:

   The percentage of wrong predictions made by the model -> Option A

4. Quick Check:

   Error rate = wrong predictions percentage [OK]

Hint: Error rate means how often the model is wrong [OK]

Common Mistakes:

• Confusing error rate with training time
• Thinking error rate counts features
• Mixing error rate with dataset size

2. Which of the following is the correct way to calculate error rate given total_predictions and wrong_predictions?

easy

A. error_rate = total_predictions / wrong_predictions

B. error_rate = total_predictions - wrong_predictions

C. error_rate = wrong_predictions * total_predictions

D. error_rate = wrong_predictions / total_predictions

Solution

1. Step 1: Recall error rate formula

   Error rate is the fraction of wrong predictions out of all predictions made.

2. Step 2: Match formula to options

   error_rate = wrong_predictions / total_predictions correctly divides wrong predictions by total predictions to get error rate.

3. Final Answer:

   error_rate = wrong_predictions / total_predictions -> Option D

4. Quick Check:

   Error rate = wrong / total [OK]

Hint: Divide wrong predictions by total predictions [OK]

Common Mistakes:

• Reversing numerator and denominator
• Multiplying instead of dividing
• Subtracting counts instead of dividing

3. Given the following code, what is the printed error rate?

total = 100
wrong = 7
error_rate = wrong / total
print(f"Error rate: {error_rate:.2f}")

medium

A. Error rate: 7.00

B. Error rate: 0.07

C. Error rate: 0.70

D. Error rate: 0.007

Solution

1. Step 1: Calculate error rate value

   error_rate = 7 / 100 = 0.07

2. Step 2: Format output to 2 decimals

   Formatted as 0.07 in the print statement.

3. Final Answer:

   Error rate: 0.07 -> Option B

4. Quick Check:

   7 divided by 100 = 0.07 [OK]

Hint: Divide wrong by total and format to two decimals [OK]

Common Mistakes:

• Confusing 7% with 7.0
• Multiplying instead of dividing
• Misreading decimal places

4. A model's error rate is unexpectedly high. Which of the following is the best first step in failure analysis?

medium

A. Check the data for incorrect labels or noise

B. Increase the number of training epochs immediately

C. Add more layers to the model without checking data

D. Reduce the size of the test dataset

Solution

1. Step 1: Understand failure analysis purpose

   Failure analysis looks for root causes of errors, often starting with data quality.

2. Step 2: Evaluate options for best first step

   Checking data labels or noise is the most direct way to find why errors happen.

3. Final Answer:

   Check the data for incorrect labels or noise -> Option A

4. Quick Check:

   Start failure analysis by checking data quality [OK]

Hint: Start failure analysis by checking data quality [OK]

Common Mistakes:

• Jumping to model changes without data check
• Ignoring data errors as cause
• Changing test set size instead of fixing errors

5. You have a model with 10,000 predictions and 500 errors. After failure analysis, you find 200 errors caused by mislabeled data. What is the corrected error rate after fixing labels?

hard

A. 0.07

B. 0.05

C. 0.03

D. 0.02

Solution

1. Step 1: Calculate original error rate

   Original errors = 500, total = 10,000, so error rate = 500/10,000 = 0.05

2. Step 2: Remove errors due to mislabeled data

   Corrected errors = 500 - 200 = 300

3. Step 3: Calculate corrected error rate

   Corrected error rate = 300 / 10,000 = 0.03

4. Final Answer:

   0.03 -> Option C

5. Quick Check:

   (500-200)/10000 = 0.03 [OK]

Hint: Subtract mislabeled errors before dividing [OK]

Common Mistakes:

• Not removing mislabeled errors
• Dividing mislabeled errors by total
• Adding mislabeled errors instead of subtracting