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ee.Reducer.ridgeRegression
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创建一个计算岭回归的 reducer,其中包含 numX 个自变量(不包括常量)和 numY 个因变量。岭回归是一种 Tikhonov 正则化,它通过对回归系数的大小施加惩罚来缩小回归系数。在此岭回归实现中,无需包含偏差的常量值。
第一个输出是维度为 (numX + 1, numY) 的系数数组;每列包含相应因变量的系数,最后一列包含因变量的截距。其他输出包括每个因变量的残差平方根向量和每个因变量的 p 值向量。如果系统欠定,例如输入数量小于 numX + 1,则输出为 null。
用法 | 返回 |
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ee.Reducer.ridgeRegression(numX, numY, lambda) | 缩减器 |
参数 | 类型 | 详细信息 |
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numX | 整数 | 被回归的自变量的数量。 |
numY | 整数,默认值:1 | 因变量的数量。 |
lambda | 浮点数,默认值:0.1 | 正则化形参。 |
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最后更新时间 (UTC):2025-07-26。
[null,null,["最后更新时间 (UTC):2025-07-26。"],[[["\u003cp\u003eCreates a reducer for ridge regression, a regularization method that shrinks regression coefficients to prevent overfitting.\u003c/p\u003e\n"],["\u003cp\u003eOutputs include regression coefficients, root mean square of residuals, and p-values for each dependent variable.\u003c/p\u003e\n"],["\u003cp\u003eRequires specifying the number of independent and dependent variables, along with an optional regularization parameter (lambda).\u003c/p\u003e\n"],["\u003cp\u003eThe reducer automatically handles the intercept term, so there's no need to add a constant value for bias.\u003c/p\u003e\n"],["\u003cp\u003eOutputs will be null if the system is underdetermined, meaning there are fewer input data points than independent variables plus one.\u003c/p\u003e\n"]]],[],null,["# ee.Reducer.ridgeRegression\n\nCreates 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.\n\n\u003cbr /\u003e\n\nThe 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.\n\n| Usage | Returns |\n|-----------------------------------------------------------|---------|\n| `ee.Reducer.ridgeRegression(numX, `*numY* `, `*lambda*`)` | Reducer |\n\n| Argument | Type | Details |\n|----------|---------------------|------------------------------------------------------|\n| `numX` | Integer | the number of independent variables being regressed. |\n| `numY` | Integer, default: 1 | the number of dependent variables. |\n| `lambda` | Float, default: 0.1 | Regularization parameter. |"]]