AI-900: What is Feature Engineering in Machine Learning?

Feature engineering is a crucial technique in machine learning used to create new features from existing data, improving model performance. Learn about feature generation methods and best practices for feature engineering in Azure AI and machine learning projects.

Question

Which of these methods is used to generate additional features? Select the correct option.

A. Model evaluation
B. Feature engineering
C. Feature selection
D. Model training

Answer

B. Feature engineering

Explanation

Feature engineering is a key step in the machine learning process that involves creating new features or transforming existing features to improve the performance of machine learning models. The goal of feature engineering is to extract relevant information from raw data and represent it in a way that enhances the predictive power of the model.

Some common techniques used in feature engineering include:

1. Feature transformation: This involves applying mathematical functions to existing features, such as logarithmic transformation, square root transformation, or normalization, to change the distribution or scale of the data.
2. Feature combination: New features can be created by combining existing features through mathematical operations like addition, subtraction, multiplication, or division.
3. Domain-specific features: Incorporating domain knowledge to create new features that are specific to the problem at hand can significantly improve model performance.
4. Encoding categorical variables: Converting categorical variables into numerical representations, such as one-hot encoding or ordinal encoding, allows machine learning algorithms to process categorical data effectively.

In contrast, the other options mentioned in the question are related to different aspects of the machine learning process:

• Model evaluation: This step involves assessing the performance of a trained model using metrics like accuracy, precision, recall, or F1 score.
• Feature selection: This process focuses on identifying the most relevant features from the existing feature set, eliminating irrelevant or redundant features to improve model efficiency and reduce overfitting.
• Model training: This is the process of using the selected features and a chosen algorithm to train a machine learning model on the available data.

In summary, feature engineering is the method used to generate additional features in machine learning, while the other options relate to different stages of the machine learning pipeline.

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