Feature engineering: Difference between revisions

[[Feature engineering]] is a crucial process in [[machine learning]] that involves selecting, extracting, and transforming relevant [[feature]]s or variables from raw data to enhance the [[accuracy]] and performance of [[machine learning models]]. This complex task necessitates domain knowledge, creativity, and proficiency with data manipulation techniques. The goal of feature engineering is to turn raw [[data]] into an informative representation that can be easily comprehended by machine learning models.

[[Feature]]s in [[machine learning]] refer to attributes or characteristics of [[data]] that can be used to describe or distinguish different [[class]]es or groups. Features typically appear as columns within a [[dataset]], with each row representing an [[example]] or [[data point]]. For instance, when looking at houses from a dataset, features might include their number of bedrooms, living room size, age of the house and location.

Features are integral in machine learning, as they form the basis for understanding patterns and making predictions. Unfortunately, not all features are equally valuable; some may be irrelevant, redundant, or noisy which negatively impacts model performance. Therefore, feature engineering plays an essential role in identifying and selecting pertinent and informative features for a given problem.

Feature engineering is essential in machine learning for several reasons. Firstly, it improves performance and [[accuracy]] of [[models]] by providing a more informative representation of data. Secondly, it reduces [[dimensionality]] by eliminating irrelevant or redundant features which simplifies the learning process and increases computational efficiency. Thirdly, feature engineering helps address issues like [[overfitting]] or [[underfitting]] by maintaining an appropriate balance between [[bias]] and [[variance]]. Finally, feature engineering improves the interpretability and explainability of machine learning models - essential qualities required in many real-world applications.

===Feature Selection===

[[Feature selection]] is the process of selecting a subset of relevant features from an expansive set. This can be done through various techniques like [[correlation analysis]], [[mutual information]], [[chi-square tests]] and [[recursive feature elimination]]. The aim is to reduce data dimensionality while maintaining or improving the performance of a machine learning model.

===Feature Extraction===

[[Feature extraction]] is the process of creating new features from existing data through various mathematical or statistical transformations. Examples of feature extraction techniques include [[Principal Component Analysis]] (PCA), [[Singular Value Decomposition]] (SVD), and [[Non-negative Matrix Factorization]] (NMF). The goal of feature extraction is to create a more informative and compact representation of data which could then enhance machine learning models' performance.

===Feature Transformation===

[[Feature transformation]] involves altering the original features by applying mathematical or statistical functions such as logarithmic, exponential or power functions. The purpose of feature transformation is to [[normalize]] data or make it more suitable for a machine learning model. Common feature transformation techniques include [[scaling]], [[centering]] and [[normalization]].

[[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]]