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  2. Continuous or discrete variable - Wikipedia

    en.wikipedia.org/.../Continuous_or_discrete_variable

    A continuous variable is a variable whose value is obtained by measuring, i.e., one which can take on an uncountable set of values. For example, a variable over a non-empty range of the real numbers is continuous, if it can take on any value in that range. The reason is that any range of real numbers between and with is uncountable, with ...

  3. Logistic regression - Wikipedia

    en.wikipedia.org/wiki/Logistic_regression

    In regression analysis, logistic regression[1] (or logit regression) estimates the parameters of a logistic model (the coefficients in the linear or non linear combinations). In binary logistic regression there is a single binary dependent variable, coded by an indicator variable, where the two values are labeled "0" and "1", while the ...

  4. Kolmogorov–Smirnov test - Wikipedia

    en.wikipedia.org/wiki/Kolmogorov–Smirnov_test

    Illustration of the Kolmogorov–Smirnov statistic. The red line is a model CDF, the blue line is an empirical CDF, and the black arrow is the KS statistic.. Kolmogorov–Smirnov test (K–S test or KS test) is a nonparametric test of the equality of continuous (or discontinuous, see Section 2.2), one-dimensional probability distributions that can be used to test whether a sample came from a ...

  5. Entropy (information theory) - Wikipedia

    en.wikipedia.org/wiki/Entropy_(information_theory)

    The Shannon entropy is restricted to random variables taking discrete values. The corresponding formula for a continuous random variable with probability density function f ( x ) with finite or infinite support X {\displaystyle \mathbb {X} } on the real line is defined by analogy, using the above form of the entropy as an expectation: [ 10 ] : 224

  6. Naive Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Naive_Bayes_classifier

    Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem. Maximum-likelihood training can be done by evaluating a closed-form expression , [ 2 ] : 718 which takes linear time , rather than by expensive iterative approximation as used for many ...

  7. Central limit theorem - Wikipedia

    en.wikipedia.org/wiki/Central_limit_theorem

    Lindeberg's CLT. In probability theory, the central limit theorem (CLT) states that, under appropriate conditions, the distribution of a normalized version of the sample mean converges to a standard normal distribution. This holds even if the original variables themselves are not normally distributed. There are several versions of the CLT, each ...

  8. Outline of discrete mathematics - Wikipedia

    en.wikipedia.org/wiki/Outline_of_discrete...

    Outline of discrete mathematics. Discrete mathematics is the study of mathematical structures that are fundamentally discrete rather than continuous. In contrast to real numbers that have the property of varying "smoothly", the objects studied in discrete mathematics – such as integers, graphs, and statements in logic [1] – do not vary ...

  9. Generative model - Wikipedia

    en.wikipedia.org/wiki/Generative_model

    P ( X , Y ) {\displaystyle P (X,Y)} on a given observable variable X and target variable Y; [1] A generative model can be used to "generate" random instances (outcomes) of an observation x. [2] A discriminative model is a model of the conditional probability. P ( Y ∣ X = x ) {\displaystyle P (Y\mid X=x)} of the target Y, given an observation x.

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