anchor.diff:  import 'package:ml_algo/src/classifier/linear/softmax_regressor/gradient_softmax_regressor.dart';
   import 'package:ml_algo/src/classifier/linear/linear_classifier.dart';
  +import 'package:ml_algo/src/helpers/features_target_split.dart';
   import 'package:ml_algo/src/model_selection/assessable.dart';
   import 'package:ml_algo/src/solver/linear/gradient/learning_rate_generator/learning_rate_type.dart';
  +import 'package:ml_dataframe/ml_dataframe.dart';
   import 'package:ml_linalg/dtype.dart';
   import 'package:ml_linalg/matrix.dart';
   
  
positive.code:         LearningRateType learningRateType,
          Matrix initialWeights,
          DType dtype,
        }) = GradientSoftmaxRegressor;
  }
  
negative.code:   ///
    /// Parameters:
    ///
    /// [fittingData] A [DataFrame] with observations, that will be used by the
    /// classifier to learn coefficients of the hyperplane, which divides the
    /// features space, forming classes of the features. Should contain target
    /// column id (index or name)
    ///
    /// [targetIndices] A collection of indices of encoded target columns (a
    /// column, that contains class labels or outcomes for the associated
    /// features)
    ///
    /// [targetNames] A collection of strings, that serves as names for the
    /// encoded target columns (a column, that contains class labels or outcomes
    /// for the associated features)
    ///
    /// [iterationsLimit] A number of fitting iterations. Default value is 100
    ///
    /// [initialLearningRate] A value, defining velocity of the convergence of the
    /// gradient descent solver. Default value is 1e-3
  