Topic 1 Question 153
You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent. Which data transformation strategy would likely improve the performance of your classifier?
Modify the target variable using the Box-Cox transformation.
Z-normalize all the numeric features.
Oversample the fraudulent transaction 10 times.
Log transform all numeric features.
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- 正解だと思う選択肢: C
The answer is C beacause it's the only way to improve model performance. Box-Cox transformation: transform feature values according to normal distribution Z-normalization: transform feature values according to x_new = (x – μ) / σ (so {x_new} have mean 0 and std dev 1) Log transform: just log transformation Also, the Random Forest algorithm is not a distance-based model but it is a tree-based model, there's no need of normalization process.
👍 3Scipione_2023/02/16 - 正解だと思う選択肢: C👍 1TNT872023/02/09
- 正解だと思う選択肢: C
See #60! The End. Good luck everyone!!!
👍 1M252023/05/10
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