ee.Reducer.ridgeRegression
Creates a reducer that computes a ridge regression with numX independent variables (not including constant) followed by numY dependent variables. Ridge regression is a form of Tikhonov regularization which shrinks the regression coefficients by imposing a penalty on their size. With this implementation of ridge regression there NO NEED to include a constant value for bias.
The first output is a coefficients array with dimensions (numX + 1, numY); each column contains the coefficients for the corresponding dependent variable plus the intercept for the dependent variable in the last column. Additional outputs are a vector of the root mean square of the residuals of each dependent variable and a vector of p-values for each dependent variable. Outputs are null if the system is underdetermined, e.g., the number of inputs is less than numX + 1.
| Usage | Returns |
|---|
ee.Reducer.ridgeRegression(numX, numY, lambda) | Reducer |
| Argument | Type | Details |
|---|
numX | Integer | the number of independent variables being regressed. |
numY | Integer, default: 1 | the number of dependent variables. |
lambda | Float, default: 0.1 | Regularization parameter. |
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Last updated 2024-07-13 UTC.
[null,null,["Last updated 2024-07-13 UTC."],[[["Creates a reducer for ridge regression, a regularization method that shrinks regression coefficients to prevent overfitting."],["Outputs include regression coefficients, root mean square of residuals, and p-values for each dependent variable."],["Requires specifying the number of independent and dependent variables, along with an optional regularization parameter (lambda)."],["The reducer automatically handles the intercept term, so there's no need to add a constant value for bias."],["Outputs will be null if the system is underdetermined, meaning there are fewer input data points than independent variables plus one."]]],[]]