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  2. One-hot - Wikipedia

    en.wikipedia.org/wiki/One-hot

    An example of ordinal data would be the ratings on a test ranging from A to F, which could be ranked using numbers from 6 to 1. Since there is no quantitative relationship between nominal variables' individual values, using ordinal encoding can potentially create a fictional ordinal relationship in the data. [9] Therefore, one-hot encoding is ...

  3. Dummy variable (statistics) - Wikipedia

    en.wikipedia.org/wiki/Dummy_variable_(statistics)

    In machine learning this is known as one-hot encoding. Dummy variables are commonly used in regression analysis to represent categorical variables that have more than two levels, such as education level or occupation.

  4. Encoding (memory) - Wikipedia

    en.wikipedia.org/wiki/Encoding_(memory)

    Semantic encoding is the processing and encoding of sensory input that has particular meaning or can be applied to a context. Various strategies can be applied such as chunking and mnemonics to aid in encoding, and in some cases, allow deep processing, and optimizing retrieval.

  5. Feature (machine learning) - Wikipedia

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

    Examples of categorical features include gender, color, and zip code. Categorical features typically need to be converted to numerical features before they can be used in machine learning algorithms. This can be done using a variety of techniques, such as one-hot encoding, label encoding, and ordinal encoding.

  6. Level of measurement - Wikipedia

    en.wikipedia.org/wiki/Level_of_measurement

    Level of measurement or scale of measure is a classification that describes the nature of information within the values assigned to variables. [1] Psychologist Stanley Smith Stevens developed the best-known classification with four levels, or scales, of measurement: nominal, ordinal, interval, and ratio.

  7. Encoding specificity principle - Wikipedia

    en.wikipedia.org/wiki/Encoding_specificity_principle

    The encoding specificity principle is the general principle that matching the encoding contexts of information at recall assists in the retrieval of episodic memories. It provides a framework for understanding how the conditions present while encoding information relate to memory and recall of that information.

  8. Levels of Processing model - Wikipedia

    en.wikipedia.org/wiki/Levels_of_Processing_model

    Modern studies show an increased effect of levels-of-processing in Alzheimer patients. Specifically, there is a significantly higher recall value for semantically encoded stimuli over physically encoded stimuli. In one such experiment, subjects maintained a higher recall value in words chosen by meaning over words selected by numerical order. [26]

  9. Hippocampal memory encoding and retrieval - Wikipedia

    en.wikipedia.org/wiki/Hippocampal_memory...

    After encoding, the hippocampus is capable of going through the retrieval process. The retrieval process consists of accessing stored information; this allows learned behaviors to experience conscious depiction and execution. [1] Encoding and retrieval are both affected by neurodegenerative and anxiety disorders and epilepsy.