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  2. Feature selection - Wikipedia

    en.wikipedia.org/wiki/Feature_selection

    Embedded methods are a catch-all group of techniques which perform feature selection as part of the model construction process. The exemplar of this approach is the LASSO method for constructing a linear model, which penalizes the regression coefficients with an L1 penalty, shrinking many of them to zero.

  3. Relief (feature selection) - Wikipedia

    en.wikipedia.org/wiki/Relief_(feature_selection)

    Relief is an algorithm developed by Kira and Rendell in 1992 that takes a filter-method approach to feature selection that is notably sensitive to feature interactions. [1] [2] It was originally designed for application to binary classification problems with discrete or numerical features.

  4. Dimensionality reduction - Wikipedia

    en.wikipedia.org/wiki/Dimensionality_reduction

    The process of feature selection aims to find a suitable subset of the input variables (features, or attributes) for the task at hand.The three strategies are: the filter strategy (e.g., information gain), the wrapper strategy (e.g., accuracy-guided search), and the embedded strategy (features are added or removed while building the model based on prediction errors).

  5. Embedded case study - Wikipedia

    en.wikipedia.org/wiki/Embedded_case_study

    An embedded case study is a case study containing more than one sub-unit of analysis (Yin, 2003). Similar to a case study, an embedded case study methodology provides a means of integrating quantitative and qualitative methods into a single research study (Scholz & Tietje, 2002; Yin 2003). However, the identification of sub-units allows for a ...

  6. 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 ...

  7. Kanade–Lucas–Tomasi feature tracker - Wikipedia

    en.wikipedia.org/wiki/Kanade–Lucas–Tomasi...

    In the second paper Tomasi and Kanade [2] used the same basic method for finding the registration due to the translation but improved the technique by tracking features that are suitable for the tracking algorithm. The proposed features would be selected if both the eigenvalues of the gradient matrix were larger than some threshold.

  8. Should you throw out your black plastic cooking utensils? - AOL

    www.aol.com/lifestyle/black-plastic-spatulas...

    Liu says that she and her research team have submitted a correction to the journal, which should be published soon. But this may not change the conclusion. Despite the mathematical error, Liu says ...

  9. Design of experiments - Wikipedia

    en.wikipedia.org/wiki/Design_of_experiments

    The use of a sequence of experiments, where the design of each may depend on the results of previous experiments, including the possible decision to stop experimenting, is within the scope of sequential analysis, a field that was pioneered [12] by Abraham Wald in the context of sequential tests of statistical hypotheses. [13]