MLS-C01 · Question #201
MLS-C01 Question #201: Real Exam Question with Answer & Explanation
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Question
A global financial company is using machine learning to automate its loan approval process. The company has a dataset of customer information. The dataset contains some categorical fields, such as customer location by city and housing status. The dataset also includes financial fields in different units, such as account balances in US dollars and monthly interest in US cents. The company's data scientists are using a gradient boosting regression model to infer the credit score for each customer. The model has a training accuracy of 99% and a testing accuracy of 75%. The data scientists want to improve the model's testing accuracy. Which process will improve the testing accuracy the MOST?
Options
- AUse a one-hot encoder for the categorical fields in the dataset. Perform standardization on
- BUse tokenization of the categorical fields in the dataset. Perform binning on the financial
- CUse a label encoder for the categorical fields in the dataset. Perform L1 regularization on
- DUse a logarithm transformation on the categorical fields in the dataset. Perform binning on
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