What is a key difference in feature engineering tasks for structured data compared to unstructured data in the context of machine learning?

What is a key difference in feature engineering tasks for structured data compared to unstructured data in the context of machine learning?
A . Feature engineering for structured data is not necessary as the data is already in a usable format, whereas for unstructured data, extensive preprocessing is always required
B . Feature engineering for structured data often involves tasks such as normalization and handling missing values, while for unstructured data, it involves tasks such as tokenization and vectorization
C . Feature engineering tasks for structured data and unstructured data are identical and do not vary based on data type
D . Feature engineering for structured data focuses on image recognition, whereas for unstructured
data, it focuses on numerical data analysis

View Answer

Answer: B

Explanation:

Correct option:

Feature engineering for structured data often involves tasks such as normalization and handling missing values, while for unstructured data, it involves tasks such as tokenization and vectorization

Feature engineering for structured data typically includes tasks like normalization, handling missing values, and encoding categorical variables. For unstructured data, such as text or images, feature engineering involves different tasks like tokenization (breaking down text into tokens), vectorization (converting text or images into numerical vectors), and extracting features that can represent the content meaningfully.

Incorrect options:

Feature engineering for structured data focuses on image recognition, whereas for unstructured data, it focuses on numerical data analysis – Structured data can include numerical and categorical data, while unstructured data includes text, images, audio, etc. The focus is not limited to image recognition or numerical data analysis.

Feature engineering for structured data is not necessary as the data is already in a usable format, whereas for unstructured data, extensive preprocessing is always required – Feature engineering is important for both structured and unstructured data. While structured data may require less preprocessing, tasks like normalization and handling missing values are still crucial. Unstructured data typically requires more extensive preprocessing.

Feature engineering tasks for structured data and unstructured data are identical and do not vary based on data type – Feature engineering tasks vary significantly between structured and unstructured data due to the inherent differences in data types and the requirements for preprocessing each type.

Reference: https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/feature-engineering.html