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  2. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    The datasets are classified, based on the licenses, as Open data and Non-Open data. The datasets from various governmental-bodies are presented in List of open government data sites. The datasets are ported on open data portals. They are made available for searching, depositing and accessing through interfaces like Open API. The datasets are ...

  3. Regression analysis - Wikipedia

    en.wikipedia.org/wiki/Regression_analysis

    In linear regression, the model specification is that the dependent variable, is a linear combination of the parameters (but need not be linear in the independent variables). For example, in simple linear regression for modeling n {\displaystyle n} data points there is one independent variable: x i {\displaystyle x_{i}} , and two parameters, β ...

  4. Ordinary least squares - Wikipedia

    en.wikipedia.org/wiki/Ordinary_least_squares

    In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one [clarification needed] effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values ...

  5. Linear regression - Wikipedia

    en.wikipedia.org/wiki/Linear_regression

    A model with exactly one explanatory variable is a simple linear regression; a model with two or more explanatory variables is a multiple linear regression. [1] This term is distinct from multivariate linear regression , which predicts multiple correlated dependent variables rather than a single dependent variable.

  6. Leverage (statistics) - Wikipedia

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

    To gain intuition for this formula, note that ^ = captures the potential for an observation to affect the regression parameters, and therefore () ^ captures the actual influence of that observations' deviations from its fitted value on the regression parameters. The formula then divides by () to account for the fact that we remove the ...

  7. Linear least squares - Wikipedia

    en.wikipedia.org/wiki/Linear_least_squares

    It is one approach to handling the "errors in variables" problem, and is also sometimes used even when the covariates are assumed to be error-free. Linear Template Fit (LTF) [7] combines a linear regression with (generalized) least squares in order to determine the best estimator. The Linear Template Fit addresses the frequent issue, when the ...