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  2. Dummy variable (statistics) - Wikipedia

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

    Dummy variables are commonly used in regression analysis to represent categorical variables that have more than two levels, such as education level or occupation. In this case, multiple dummy variables would be created to represent each level of the variable, and only one dummy variable would take on a value of 1 for each observation.

  3. One-hot - Wikipedia

    en.wikipedia.org/wiki/One-hot

    Another downside of one-hot encoding is that it causes multicollinearity between the individual variables, which potentially reduces the model's accuracy. [citation needed] Also, if the categorical variable is an output variable, you may want to convert the values back into a categorical form in order to present them in your application. [10]

  4. Feature (machine learning) - Wikipedia

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

    This can be done using a variety of techniques, such as one-hot encoding, label encoding, and ordinal encoding. The type of feature that is used in feature engineering depends on the specific machine learning algorithm that is being used. Some machine learning algorithms, such as decision trees, can handle both numerical and categorical features.

  5. Feature hashing - Wikipedia

    en.wikipedia.org/wiki/Feature_hashing

    In a typical document classification task, the input to the machine learning algorithm (both during learning and classification) is free text. From this, a bag of words (BOW) representation is constructed: the individual tokens are extracted and counted, and each distinct token in the training set defines a feature (independent variable) of each of the documents in both the training and test sets.

  6. State encoding for low power - Wikipedia

    en.wikipedia.org/wiki/State_encoding_for_low_power

    For such FSM, one-hot encoding guarantees switching of two bits for every state change. But since the number of state variables needed is equal to the number of states, as states increase, one-hot encoding becomes an impractical solution, mainly because with an increased number of inputs and outputs to the circuit, complexity and capacitive ...

  7. Multiple correspondence analysis - Wikipedia

    en.wikipedia.org/wiki/Multiple_correspondence...

    The Burt table is the symmetric matrix of all two-way cross-tabulations between the categorical variables, and has an analogy to the covariance matrix of continuous variables. Analyzing the Burt table is a more natural generalization of simple correspondence analysis , and individuals or the means of groups of individuals can be added as ...

  8. A Woman Told Friends Her Boyfriend Was Threatening Her with a ...

    www.aol.com/woman-told-friends-her-boyfriend...

    A South Dakota man is facing murder and manslaughter charges after police say he killed a woman and decapitated her. According to court documents obtained by PEOPLE, Craig Allen Nichols Jr., 32 ...

  9. Categorical distribution - Wikipedia

    en.wikipedia.org/wiki/Categorical_distribution

    function draw_categorical(n) // where n is the number of samples to draw from the categorical distribution r = 1 s = 0 for i from 1 to k // where k is the number of categories v = draw from a binomial(n, p[i] / r) distribution // where p[i] is the probability of category i for j from 1 to v z[s++] = i // where z is an array in which the results ...