public String globalInfo(){
  return "Implements stochastic gradient descent for learning" + " various linear models (binary class SVM, binary class" + " logistic regression, squared loss, Huber loss and "+ "epsilon-insensitive loss linear regression)."+ " Globally replaces all missing values and transforms nominal"+ " attributes into binary ones. It also normalizes all attributes,"+ " so the coefficients in the output are based on the normalized"+ " data.\n"+ "For numeric class attributes, the squared, Huber or "+ "epsilon-insensitve loss function must be used. Epsilon-insensitive "+ "and Huber loss may require a much higher learning rate.";
}

public String globalInfo ( ) { return "Implements stochastic gradient descent for learning" + " various linear models (binary class SVM, binary class" + " logistic regression, squared loss, Huber loss and " + "epsilon-insensitive loss linear regression)." + " Globally replaces all missing values and transforms nominal" + " attributes into binary ones. It also normalizes all attributes," + " so the coefficients in the output are based on the normalized" + " data.\n" + "For numeric class attributes, the squared, Huber or " + "epsilon-insensitve loss function must be used. Epsilon-insensitive " + "and Huber loss may require a much higher learning rate." ; }
returns a string describing classifier
