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==What are features in machine learning?== | ==What are features in machine learning?== | ||
[[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 | 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. | ||
==Why is feature engineering important?== | ==Why is feature engineering important?== | ||
Feature engineering is | 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. | ||
==What are the types of feature engineering?== | ==What are the types of feature engineering?== | ||
Feature engineering can be broadly classified into three main types: [[feature selection]], [[feature extraction]], and [[feature transformation]]. | Feature engineering can be broadly classified into three main types: [[feature selection]], [[feature extraction]], and [[feature transformation]]. | ||
===Feature | ===Feature Selection=== | ||
[[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 | ===Feature Extraction=== | ||
[[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 | ===Feature Transformation=== | ||
[[Feature transformation]] involves | [[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]]. | ||
==How is feature engineering done in practice?== | ==How is feature engineering done in practice?== | ||
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By choosing the right features, we can help the computer learn more quickly and accurately. It's like having the right tools to put a puzzle together faster and with fewer mistakes. | By choosing the right features, we can help the computer learn more quickly and accurately. It's like having the right tools to put a puzzle together faster and with fewer mistakes. | ||
[[Category:Terms]] [[Category:Machine learning terms]] | [[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]] |