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Feature engineering: Difference between revisions
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==Introduction== | ==Introduction== | ||
[[Feature engineering]] is a critical process in [[machine learning]] that involves selecting, extracting, and transforming relevant [[feature]]s or variables from raw [[data]] to improve the accuracy and performance of [[machine learning models]]. Feature engineering is a complex and challenging process that requires domain knowledge, creativity, and expertise in data manipulation techniques. The objective of feature engineering is to transform raw data into a more suitable and informative representation that can be easily understood by machine learning models. | [[Feature engineering]] is a critical process in [[machine learning]] that involves selecting, extracting, and transforming relevant [[feature]]s or variables from raw [[data]] to improve the accuracy and performance of [[machine learning models]]. Feature engineering is a complex and challenging process that requires domain knowledge, creativity, and expertise in data manipulation techniques. The objective of feature engineering is to transform raw data into a more suitable and informative representation that can be easily understood by machine learning models. | ||
==What are features in machine learning?== | ==What are features in machine learning?== | ||
Features in machine learning refer to the attributes or characteristics of the data that can be used to describe or differentiate different | Features in machine learning refer to the attributes or characteristics of the data that can be used to describe or differentiate different [[class]]es or groups. Features are typically represented as columns in a [[dataset]], where each row corresponds to an [[example]] or [[data point]]. For example, in a dataset containing information about houses, features may include the number of bedrooms, the size of the living room, the age of the house, and the location of the house. | ||
Features are essential in machine learning, as they provide the basis for learning patterns and making predictions. However, not all features are equally important, and some may be irrelevant, redundant, or noisy, which can negatively impact the performance of machine learning models. Therefore, the process of feature engineering is crucial in identifying and selecting the most relevant and informative features for a particular problem. | Features are essential in machine learning, as they provide the basis for learning patterns and making predictions. However, not all features are equally important, and some may be irrelevant, redundant, or noisy, which can negatively impact the performance of machine learning models. Therefore, the process of feature engineering is crucial in identifying and selecting the most relevant and informative features for a particular problem. | ||
==Why is feature engineering important?== | ==Why is feature engineering important?== | ||
Feature engineering is important in machine learning for several reasons. First, it helps to improve the performance and accuracy of machine learning models by providing a more informative and discriminative representation of the data. Second, it helps to reduce the dimensionality of the data by removing irrelevant or redundant features, which can simplify the learning process and improve computational efficiency. Third, it can help to address issues such as overfitting and underfitting by providing a better balance between bias and variance. Finally, feature engineering can help to enhance the interpretability and explainability of machine learning models, which is essential in many real-world applications. | Feature engineering is important in machine learning for several reasons. First, it helps to improve the performance and [[accuracy]] of machine learning models by providing a more informative and discriminative representation of the data. Second, it helps to reduce the [[dimensionality]] of the data by removing irrelevant or redundant features, which can simplify the learning process and improve computational efficiency. Third, it can help to address issues such as [[overfitting]] and [[underfitting]] by providing a better balance between [[bias]] and [[variance]]. Finally, feature engineering can help to enhance the interpretability and explainability of machine learning models, which is essential 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 selection=== | ===Feature selection=== | ||
Feature selection involves selecting a subset of relevant features from a larger set of features. This can be done using various techniques such as correlation analysis, mutual information, chi-square tests, and recursive feature elimination. The goal of feature selection is to reduce the dimensionality of the data while maintaining or improving the performance of the machine learning model. | [[Feature selection]] involves selecting a subset of relevant features from a larger set of features. This can be done using various techniques such as [[correlation analysis]], [[mutual information]], [[chi-square tests]], and [[recursive feature elimination]]. The goal of feature selection is to reduce the dimensionality of the data while maintaining or improving the performance of the machine learning model. | ||
===Feature extraction=== | ===Feature extraction=== | ||
Feature extraction involves creating new features from existing features by applying 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 the data that can improve the performance of machine learning models. | [[Feature extraction]] involves creating new features from existing features by applying 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 the data that can improve the performance of machine learning models. | ||
===Feature transformation=== | ===Feature transformation=== | ||
Feature transformation involves transforming the original features by applying mathematical or statistical functions such as logarithmic, exponential, or power functions. The goal of feature transformation is to normalize the data or make it more suitable for a particular machine learning model. Examples of feature transformation techniques include scaling, centering, and normalization. | [[Feature transformation]] involves transforming the original features by applying mathematical or statistical functions such as logarithmic, exponential, or power functions. The goal of feature transformation is to [[normalize]] the data or make it more suitable for a particular machine learning model. Examples of feature transformation techniques include [[scaling]], [[centering]], and [[normalization]]. | ||
==How is feature engineering done in practice?== | ==How is feature engineering done in practice?== | ||