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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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-07-13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."]]],[]]