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  2. Feature engineering - Wikipedia

    en.wikipedia.org/wiki/Feature_engineering

    Feature engineering in machine learning and statistical modeling involves selecting, creating, transforming, and extracting data features. Key components include feature creation from existing data, transforming and imputing missing or invalid features, reducing data dimensionality through methods like Principal Components Analysis (PCA), Independent Component Analysis (ICA), and Linear ...

  3. Feature (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Feature_(machine_learning)

    In feature engineering, two types of features are commonly used: numerical and categorical. Numerical features are continuous values that can be measured on a scale. Examples of numerical features include age, height, weight, and income. Numerical features can be used in machine learning algorithms directly. [citation needed]

  4. Feature learning - Wikipedia

    en.wikipedia.org/wiki/Feature_learning

    In machine learning (ML), feature learning or representation learning [2] is a set of techniques that allow a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a ...

  5. List of engineering branches - Wikipedia

    en.wikipedia.org/wiki/List_of_engineering_branches

    Engineering is the discipline and profession that applies scientific theories, mathematical methods, and empirical evidence to design, create, and analyze technological solutions, balancing technical requirements with concerns or constraints on safety, human factors, physical limits, regulations, practicality, and cost, and often at an industrial scale.

  6. Feature scaling - Wikipedia

    en.wikipedia.org/wiki/Feature_scaling

    Feature standardization makes the values of each feature in the data have zero-mean (when subtracting the mean in the numerator) and unit-variance. This method is widely used for normalization in many machine learning algorithms (e.g., support vector machines , logistic regression , and artificial neural networks ).

  7. Feature selection - Wikipedia

    en.wikipedia.org/wiki/Feature_selection

    Filter feature selection is a specific case of a more general paradigm called structure learning.Feature selection finds the relevant feature set for a specific target variable whereas structure learning finds the relationships between all the variables, usually by expressing these relationships as a graph.

  8. Software feature - Wikipedia

    en.wikipedia.org/wiki/Software_feature

    "Distress Selection" software feature in the photo editing program GIMP Menu showing a list of available features in the X Window System terminal emulator program xterm. A feature is "a prominent or distinctive user-visible aspect, quality, or characteristic of a software system or systems", as defined by Kang et al. [1] At the implementation level, "it is a structure that extends and modifies ...

  9. Engineering design process - Wikipedia

    en.wikipedia.org/wiki/Engineering_design_process

    The engineering design process, also known as the engineering method, is a common series of steps that engineers use in creating functional products and processes. The process is highly iterative – parts of the process often need to be repeated many times before another can be entered – though the part(s) that get iterated and the number of such cycles in any given project may vary.