Feature Engineering Intuition
Explore the concept of feature engineering to understand how transforming raw data into meaningful features improves machine learning predictions. This lesson covers the importance of domain knowledge, common pitfalls like overfitting with raw timestamps, and practical methods for extracting features using R. Gain insights into row-based and column-based feature engineering techniques to prepare robust data for modeling.
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Feature engineering defined
Feature engineering is the process of using business domain and technical knowledge to extract features from raw data. Extracted features transform raw data into a format that can improve the predictive performance of machine learning algorithms.
This definition has essential implications for machine learning practitioners:
Machine learning algorithms are not guaranteed to learn from raw data.
Machine learning algorithms might learn the wrong things from raw data.
Feature engineering is an art where domain expertise and technical knowledge are vital.
Feature engineering is required to craft the most valuable machine learning models. ...