enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Polynomial regression - Wikipedia

    en.wikipedia.org/wiki/Polynomial_regression

    Although polynomial regression fits a nonlinear 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. For this reason, polynomial regression is considered to be a special case of multiple linear regression. [1]

  3. Regularized least squares - Wikipedia

    en.wikipedia.org/wiki/Regularized_least_squares

    Besides feature selection described above, LASSO has some limitations. Ridge regression provides better accuracy in the case > for highly correlated variables. [3] In another case, <, LASSO selects at most variables. Moreover, LASSO tends to select some arbitrary variables from group of highly correlated samples, so there is no grouping effect.

  4. Linear least squares - Wikipedia

    en.wikipedia.org/wiki/Linear_least_squares

    Cubic, quartic and higher polynomials. For regression with high-order polynomials, the use of orthogonal polynomials is recommended. [15] Numerical smoothing and differentiation — this is an application of polynomial fitting. Multinomials in more than one independent variable, including surface fitting; Curve fitting with B-splines [12]

  5. Autoregressive moving-average model - Wikipedia

    en.wikipedia.org/wiki/Autoregressive_moving...

    Python has the statsmodelsS package which includes many models and functions for time series analysis, including ARMA. Formerly part of the scikit-learn library, it is now stand-alone and integrates well with Pandas. PyFlux has a Python-based implementation of ARIMAX models, including Bayesian ARIMAX models.

  6. 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 ]

  7. Regression analysis - Wikipedia

    en.wikipedia.org/wiki/Regression_analysis

    In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the outcome or response variable, or a label in machine learning parlance) and one or more error-free independent variables (often called regressors, predictors, covariates, explanatory ...

  8. Non-linear least squares - Wikipedia

    en.wikipedia.org/wiki/Non-linear_least_squares

    Consider a set of data points, (,), (,), …, (,), and a curve (model function) ^ = (,), that in addition to the variable also depends on parameters, = (,, …,), with . It is desired to find the vector of parameters such that the curve fits best the given data in the least squares sense, that is, the sum of squares = = is minimized, where the residuals (in-sample prediction errors) r i are ...

  9. Multinomial logistic regression - Wikipedia

    en.wikipedia.org/.../Multinomial_logistic_regression

    Multinomial logistic regression is a particular solution to classification problems that use a linear combination of the observed features and some problem-specific parameters to estimate the probability of each particular value of the dependent variable.