elasticNet(ds, yColName, xColNames, [alpha=1.0], [l1Ratio=0.5], [intercept=true], [normalize=false], [maxIter=1000], [tolerance=0.0001], [positive=false])


ds is an in-memory table or a data source usually generated by the sqlDS function.

yColName is a string indicating the column name of the dependent variable in ds.

xColNames is a string scalar/vector indicating the column names of the independent variables in ds.

alpha is a floating number representing the constant that multiplies the L1-norm. The default value is 1.0.

l1Ratio is a floating number between 0 and 1 indicating the mixing parameter. For l1Ratio = 0 the penalty is an L2 penalty; for l1Ratio = 1 it is an L1 penalty; for 0 < l1Ratio < 1, the penalty is a combination of L1 and L2. The default value is 0.5.

intercept is a Boolean value indicating whether to include the intercept in the regression. The default value is true.

normalize is a Boolean value. If true, the regressors will be normalized before regression by subtracting the mean and dividing by the L2-norm. If intercept=false, this parameter will be ignored. The default value is false.

maxIter is a positive integer indicating the maximum number of iterations. The default value is 1000.

tolerance is a floating number. The iterations stop when the improvement in the objective function value is smaller than tolerance. The default value is 0.0001.

positive is a Boolean value indicating whether to force the coefficient estimates to be positive. The default value is false.


Linear regression with combined L1 and L2 priors as regularizer.

Minimize the following objective function:

\[\dfrac{1}{2*n\_samples}* \Bigl\lVert{y - Xw} \Bigr\rVert_2^2 + alpha * l1Ratio\Bigl\lVert{w}\Bigr\rVert_1 + \dfrac{alpha*(1-l1Ratio)}{2}\Bigl\lVert{w}\Bigr\rVert_2^2\]


$ 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)
$ elasticNet(t, `y, `x0`x1);

If t is a DFS table, then the input should be a data source:

$ elasticNet(sqlDS(<select * from t>), `y, `x0`x1);