Error rate and failure analysis in Agentic AI

Introduction

Error rate tells us how often a model makes mistakes. Failure analysis helps find why and where these mistakes happen.

Checking how well a spam filter catches unwanted emails

Finding why a voice assistant misunderstands commands

Improving a self-driving car's object detection errors

Evaluating a medical test's wrong diagnosis cases

Understanding mistakes in a recommendation system

Syntax

Agentic AI

error_rate = number_of_wrong_predictions / total_predictions

# For failure analysis, review wrong cases and find patterns

Error rate is a simple fraction showing mistakes over total tries.

Failure analysis is more about looking closely at errors to improve the model.

Examples

This means the model is wrong 5% of the time.

Agentic AI

error_rate = 5 / 100  # 5 wrong out of 100 predictions

Collecting wrong predictions helps us see patterns causing errors.

Agentic AI

wrong_cases = [case for case in test_data if model.predict(case) != case.label]
# Look at wrong_cases to find common issues

Sample Model

This code trains a simple decision tree on iris data, calculates error rate, and prints some wrong predictions for failure analysis.

Agentic AI

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

# Load data
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=42)

# Train model
model = DecisionTreeClassifier(random_state=42)
model.fit(X_train, y_train)

# Predict
predictions = model.predict(X_test)

# Calculate error rate
wrong = sum(predictions != y_test)
total = len(y_test)
error_rate = wrong / total

print(f"Wrong predictions: {wrong}")
print(f"Total predictions: {total}")
print(f"Error rate: {error_rate:.2f}")

# Failure analysis: show some wrong cases
error_count = 0
for i, (pred, true) in enumerate(zip(predictions, y_test)):
    if pred != true:
        print(f"Index {i}: predicted {pred}, actual {true}")
        error_count += 1
        if error_count >= 3:  # show only first 3 errors
            break

Important Notes

Always check error rate on new data to see real performance.

Failure analysis helps find if errors happen on specific groups or conditions.

Reducing error rate improves trust in your model.

Summary

Error rate shows how often a model is wrong.

Failure analysis looks closely at mistakes to understand causes.

Both help improve machine learning models step by step.

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