Exam AI-900 topic 1 question 16 discussion

Answer is Feature Engineering. Explaination - feature engineering is applied first to generate additional features, and then feature selection is done to eliminate irrelevant, redundant, or highly correlated features. Reference - https://docs.microsoft.com/en-us/azure/architecture/data-science-process/create-features

Feature engineering

Feature Engineering is correct - While raw data fields provide the foundation for model training, feature engineering is the art of creating additional features that better highlight underlying patterns. By leveraging domain knowledge, we can derive new features that help machine learning algorithms learn more effectively.

Feature Engineering is correct - While raw data fields provide the foundation for model training, feature engineering is the art of creating additional features that better highlight underlying patterns. By leveraging domain knowledge, we can derive new features that help machine learning algorithms learn more effectively.

The answer is correct! https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/create-features

1. Feature engineering https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-features?view=azureml-api-1#feature-engineering-and-featurization Although many of the raw data fields can be used directly to train a model, it's often necessary to create additional (engineered) features that provide information that better differentiates patterns in the data. This process is called feature engineering, where the use of domain knowledge of the data is leveraged to create features that, in turn, help machine learning algorithms to learn better.

Feature Engineering is the right answer.

Feature engineering

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Feature Engineering

feature engineering

Feature Engineering

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Keyword = Generate = Engineer

Feature engineering