enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Coefficient of determination - Wikipedia

    en.wikipedia.org/wiki/Coefficient_of_determination

    Ordinary least squares regression of Okun's law.Since the regression line does not miss any of the points by very much, the R 2 of the regression is relatively high.. In statistics, the coefficient of determination, denoted R 2 or r 2 and pronounced "R squared", is the proportion of the variation in the dependent variable that is predictable from the independent variable(s).

  3. Principal component regression - Wikipedia

    en.wikipedia.org/wiki/Principal_component_regression

    The PCR method may be broadly divided into three major steps: 1. Perform PCA on the observed data matrix for the explanatory variables to obtain the principal components, and then (usually) select a subset, based on some appropriate criteria, of the principal components so obtained for further use.

  4. Out-of-bag error - Wikipedia

    en.wikipedia.org/wiki/Out-of-bag_error

    One set, the bootstrap sample, is the data chosen to be "in-the-bag" by sampling with replacement. The out-of-bag set is all data not chosen in the sampling process. When this process is repeated, such as when building a random forest , many bootstrap samples and OOB sets are created.

  5. Regression validation - Wikipedia

    en.wikipedia.org/wiki/Regression_validation

    If, for example, the out-of-sample mean squared error, also known as the mean squared prediction error, is substantially higher than the in-sample mean square error, this is a sign of deficiency in the model. A development in medical statistics is the use of out-of-sample cross validation techniques in meta-analysis.

  6. Symbolic regression - Wikipedia

    en.wikipedia.org/wiki/Symbolic_regression

    In 2022, SRBench announced the competition Interpretable Symbolic Regression for Data Science, which was held at the GECCO conference in Boston, MA. The competition pitted nine leading symbolic regression algorithms against each other on a novel set of data problems and considered different evaluation criteria.

  7. Linear regression - Wikipedia

    en.wikipedia.org/wiki/Linear_regression

    Example of a cubic polynomial regression, which is a type of linear regression. Although polynomial regression fits a curve model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E( y | x ) is linear in the unknown parameters that are estimated from the data .

  8. Multivariate adaptive regression spline - Wikipedia

    en.wikipedia.org/wiki/Multivariate_adaptive...

    For example, each line in the formula for ozone above is one basis function multiplied by its coefficient. Each basis function takes one of the following three forms: 1) a constant 1. There is just one such term, the intercept. In the ozone formula above, the intercept term is 5.2. 2) a hinge function.

  9. Ridge regression - Wikipedia

    en.wikipedia.org/wiki/Ridge_regression

    Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated. [1] It has been used in many fields including econometrics, chemistry, and engineering. [2]