lassoBasic#
- swordfish.function.lassoBasic()#
Perform lasso regression.
- Parameters:
Y (Constant) – A numeric vector indicating the dependent variables.
X (Constant) –
A numeric vector/tuple/matrix/table, indicating the independent variables.
When X is a vector/tuple, its length must be equal to the length of Y.
When X is a matrix/table, its number of rows must be equal to the length of Y.
mode (Constant) –
An integer that can take the following three values:
0 (default) : a vector of the coefficient estimates.
1: a table with coefficient estimates, standard error, t-statistics, and p-values.
2: a dictionary with the following keys: ANOVA, RegressionStat, Coefficient and Residual.
alpha (Constant) – A floating number representing the constant that multiplies the L1-norm. The default value is 1.0.
intercept (Constant) – A Boolean variable indicating whether the regression includes the intercept. If it is true, the system automatically adds a column of “1”s to X to generate the intercept. The default value is true.
normalize (Constant) – 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 (Constant) – A positive integer indicating the maximum number of iterations. The default value is 1000.
tolerance (Constant) – 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 (Constant) – A Boolean value indicating whether to force the coefficient estimates to be positive. The default value is false.
swColName (Constant) – A STRING indicating a column name of ds. The specified column is used as the sample weight. If it is not specified, the sample weight is treated as 1.
checkInput (Constant) –
A BOOLEAN value. It determines whether to enable validation check for parameters yColName, xColNames, and swColName.
If checkInput = true (default), it will check the invalid value for parameters and throw an error if the null value exists.
If checkInput = false, the invalid value is not checked.
Note
It is recommended to specify checkInput = true. If it is false, it must be ensured that there are no invalid values in the input parameters and no invalid values are generated during intermediate calculations, otherwise the returned model may be inaccurate.