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Error rate and failure analysis in Agentic AI - Cheat Sheet & Quick Revision

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beginner
What is the error rate in machine learning?
The error rate is the percentage of wrong predictions made by a model compared to the total predictions. It shows how often the model makes mistakes.
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beginner
Why is failure analysis important in machine learning?
Failure analysis helps us understand why a model makes mistakes. It identifies patterns or reasons behind errors so we can improve the model.
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beginner
How do you calculate the error rate from predictions?
Error rate = (Number of wrong predictions) ÷ (Total predictions). For example, if 10 out of 100 predictions are wrong, error rate = 10%.
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intermediate
What is a common method to perform failure analysis?
A common method is to look at the confusion matrix to see which types of errors happen most, then analyze those cases to find causes.
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intermediate
How can failure analysis improve a machine learning model?
By finding error patterns, we can fix data issues, adjust model settings, or add features. This reduces errors and makes the model better.
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What does a high error rate indicate about a model?
AThe model makes many mistakes
BThe model is very accurate
CThe model never makes mistakes
DThe model is overfitting perfectly
Which tool helps identify types of errors in classification?
AHistogram
BConfusion matrix
CScatter plot
DLine chart
What is the first step in failure analysis?
ALook at error patterns
BTrain a new model
CIgnore errors
DIncrease dataset size
If a model has 5 wrong predictions out of 50, what is the error rate?
A10%
B15%
C5%
D20%
How can failure analysis help improve a model?
ABy removing all features
BBy ignoring errors
CBy reducing dataset size
DBy finding error causes and fixing them
Explain what error rate means and how you calculate it.
Think about how many predictions are wrong out of total.
You got /3 concepts.
    Describe the steps and purpose of failure analysis in machine learning.
    Why do we look closely at mistakes?
    You got /3 concepts.

      Practice

      (1/5)
      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