Machine Learning (ML) Feature Engineering Task

A Machine Learning (ML) Feature Engineering Task is a engineering task that involves creating, selecting, and transforming features to optimize the performance of machine learning models.

• Context:
  • It can (typically) involve the use of domain expertise to identify the most informative features from raw data.
  • It can (often) require the application of various data transformation techniques such as feature scaling, feature selection, and feature extraction.
  • It can range from simple feature normalization techniques to complex feature learning methods that automatically discover the best representations.
  • It can be performed manually by data scientists or automatically through automated feature engineering tools.
  • It can significantly impact the accuracy and performance of machine learning models, as well-designed features can provide better input signals for learning algorithms.
  • ...
• Example(s):
  • a Feature Selection Task where irrelevant or redundant features are removed to improve model performance.
  • a Feature Construction Task where new features are constructed from existing data to capture hidden patterns.
  • ...
• Counter-Example(s):
  • Model Evaluation Tasks, which focus on assessing the performance of machine learning models rather than manipulating or creating features.
  • Reward Function Design Tasks, which primarily focuses on crafting functions that measure the effectiveness of actions in a learning environment, rather than feature manipulation.
  • ...
• See: Feature Selection, Feature Extraction, Dimensionality Reduction, Data Preprocessing, Machine Learning Pipeline, ML Feature Space Designing.

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