elasticNetCV

elasticNetCV(ds, yColName, xColNames, [alphas], [l1Ratio], [intercept], [normalize], [maxIter], [tolerance], [positive], [swColName], [checkInput])

The elsticNetCV function inherits all parameters of function elasticNet, with one added parameter, alphas.

alphas (optional) is a floating-point scalar or vector that represents the coefficient multiplied by the L1 norm penalty term. The default value is [0.01, 0.1, 1.0].

Implement linear regression with elastic net penalty using 5-fold cross-validation and return a model corresponding to the optimal parameters.

Return value: A dictionary containing the following keys

• modelName: the model name, which is "elasticNetCV" for this method

• coefficients: the regression coefficients

• intercept: the intercept

• dual_gap: the dual gap

• tolerance: the tolerance for the optimization

• iterations: the number of iterations

• xColNames: the column names of the independent variables in the data source

• predict: the function used for prediction

• alpha: the penalty term for cross-validation

y = [225.720746,-76.195841,63.089878,139.44561,-65.548346,2.037451,22.403987,-0.678415,37.884102,37.308288]
x0 = [2.240893,-0.854096,0.400157,1.454274,-0.977278,-0.205158,0.121675,-0.151357,0.333674,0.410599]
x1 = [0.978738,0.313068,1.764052,0.144044,1.867558,1.494079,0.761038,0.950088,0.443863,-0.103219]
t = table(y, x0, x1);

elasticNetCV(t, `y, `x0`x1);

// output
modelName->elasticNetCV
coefficients->[93.8331,13.9105]
intercept->0.5416
dual_gap->0.0037
tolerance->0.0001
iterations->5
xColNames->["x0","x1"]
predict->coordinateDescentPredict
alpha->0.01