code: function(uid) {
  
          this.logger = Logger.getLogger("ml_regression_GeneralizedLinearRegression_js");
          var jvmObject;
          if (arguments.length > 0) {
              if (arguments[0] instanceof org.apache.spark.ml.regression.GeneralizedLinearRegression) {
                  jvmObject = arguments[0];
              } else {
                  jvmObject = new org.apache.spark.ml.regression.GeneralizedLinearRegression(uid);
              }
          } else {
              jvmObject = new org.apache.spark.ml.regression.GeneralizedLinearRegression();
          }
  
          DefaultParamsWritable.call(this, jvmObject);
  
      }
docs: var GeneralizedLinearRegression = Java.type('org.apache.spark.ml.regression.GeneralizedLinearRegression'); 
  @classdesc
  :: Experimental ::
  
  Fit a Generalized Linear Model ([[https://en.wikipedia.org/wiki/Generalized_linear_model]])
  specified by giving a symbolic description of the linear predictor (link function) and
  a description of the error distribution (family).
  It supports "gaussian", "binomial", "poisson" and "gamma" as family.
  Valid link functions for each family is listed below. The first link function of each family
  is the default one.
  - "gaussian" -> "identity", "log", "inverse"
  - "binomial" -> "logit", "probit", "cloglog"
  - "poisson"  -> "log", "identity", "sqrt"
  - "gamma"    -> "inverse", "identity", "log"
  @class
  @memberof module:eclairjs/ml/regression
  @extends module:eclairjs/ml/util.DefaultParamsWritable
  @param {string} uid
  @constructor
  @function
  @name module:eclairjs/ml/regression.GeneralizedLinearRegression#<init>