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, numY); each column contains the coefficients for the corresponding dependent variable. 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.

UsageReturns
ee.Reducer.ridgeRegression(numX, numY, lambda)Reducer
ArgumentTypeDetails
numXInteger

the number of independent variables being regressed.

numYInteger, default: 1

the number of dependent variables.

lambdaFloat, default: 0.1

Regularization parameter.

Examples

JavaScript

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Python

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