AI-900: Unleashing the Power of Feature Engineering in Machine Learning

Discover the significance of feature engineering in machine learning and how it plays a pivotal role in generating additional features for enhanced model performance. Gain insights into the practical applications of feature engineering and its impact on data preprocessing. Stay ahead in the world of advanced machine learning techniques.

Question

__________ is used to generate additional features.

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

Answer

A. Feature engineering

Explanation

The correct answer is A. Feature engineering.

Feature engineering is the process of creating new features or transforming existing features to improve the performance of a machine learning model. Feature engineering can involve techniques such as scaling, normalization, encoding, binning, imputation, aggregation, and interaction. Feature engineering can help to capture the underlying patterns or relationships in the data, reduce the dimensionality of the data, and make the data more suitable for the chosen model.

Feature selection, on the other hand, is the process of choosing a subset of features that are most relevant for the prediction task.

Model evaluation is the process of measuring how well a model performs on unseen data, using metrics such as accuracy, precision, recall, F1-score, ROC curve, etc.

Model training is the process of fitting a model to the training data, using algorithms such as gradient descent, stochastic gradient descent, or Adam.

Reference

Microsoft Learn > Azure > Architecture > Team Data Science Process > Feature engineering in machine learning

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.