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When we add engineered features, we want to see if the model predicts better. Common metrics to check are accuracy for general correctness, precision and recall to understand how well the model finds true positives without many mistakes, and F1 score to balance precision and recall. These metrics show if new features help the model make clearer decisions.
Without engineered features:
TP=70 FP=30
FN=40 TN=160
With engineered features:
TP=85 FP=15
FN=25 TN=175
Total samples = 300
Explanation:
- TP (True Positives): Correctly found positive cases
- FP (False Positives): Mistakenly marked negatives as positive
- FN (False Negatives): Missed positive cases
- TN (True Negatives): Correctly found negative cases
Adding engineered features often helps the model find more true positives (higher recall) and reduce false alarms (higher precision).
Example: In email spam detection, engineered features like word counts or sender reputation help the model catch more spam (higher recall) without marking good emails as spam (higher precision).
Sometimes improving one metric lowers the other. Good features help improve both, making the model more reliable.
Good: Precision and recall both above 80%, showing the model finds most positives and makes few mistakes.
Bad: High accuracy but low recall (e.g., 95% accuracy but 30% recall) means the model misses many positives, which is risky.
Engineered features should help move metrics from bad to good by giving the model clearer clues.
Your model has 98% accuracy but only 12% recall on fraud cases. Is it good for production? Why or why not?
Answer: No, it is not good. The model misses 88% of fraud cases (low recall), which is dangerous. High accuracy is misleading because fraud is rare. You need better features or methods to improve recall.
age_group from an age column in Python using pandas?apply with a function lets us assign categories like 'young' or 'old' based on age.apply with a lambda function to create age_group correctly. df['age_group'] = df['age'] > 30 creates a boolean, not a group. The sum and mean options compute sums or means, not groups.print(df) after feature engineering?
import pandas as pd
df = pd.DataFrame({'temp_c': [0, 20, 30]})
df['temp_f'] = df['temp_c'] * 9/5 + 32
print(df)temp_c.is_adult but it gives wrong results. What is the bug?
df['is_adult'] = df['age'] > '18'
age values to a string '18', which leads to wrong boolean results.age to string '18' causes incorrect results. -> Option A