anchor.diff: +import 'package:ml_algo/src/classifier/linear/linear_classifier.dart';
   import 'package:ml_algo/src/classifier/linear/logistic_regressor/gradient_logistic_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';
  
positive.code: abstract class LogisticRegressor implements LinearClassifier, Assessable {
    /// Parameters:
    ///
    /// [trainingFeatures] A matrix 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
    ///
    /// [trainingOutcomes] A matrix with outcomes (class labels, or dependant
    /// variables) for each observation from [trainingFeatures]
    ///
    /// [iterationsLimit] A number of fitting iterations. Uses as a condition of
    /// convergence in the solver. Default value is 100
  
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)
    ///
    /// [targetIndex] An index of the target column (a column, that contains
    /// class labels or outcomes for the associated features)
    ///
    /// [targetName] A string, that serves as a name of the target column (a
    /// column, that contains class labels or outcomes for the associated
    /// features)
    ///
    /// [iterationsLimit] A number of fitting iterations. Uses as a condition of
    /// convergence in the [solver]. Default value is 100
  