LogisticRegressor.newton constructor

LogisticRegressor.newton(DataFrame trainingData, String targetName, { int iterationsLimit = iterationLimitDefaultValue, double minCoefficientsUpdate = minCoefficientsUpdateDefaultValue, double probabilityThreshold = probabilityThresholdDefaultValue, double lambda = lambdaDefaultValue, bool fitIntercept = fitInterceptDefaultValue, double interceptScale = interceptScaleDefaultValue, num positiveLabel = positiveLabelDefaultValue, num negativeLabel = negativeLabelDefaultValue, bool collectLearningData = collectLearningDataDefaultValue, DType dtype = dTypeDefaultValue, Vector? initialCoefficients, })

Creates a LogisticRegressor instance based on Newton-Raphson method

Parameters:

trainingData Observations that will be used by the classifier to learn the coefficients. Must contain targetName column.

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 optimization algorithm. Default value is 100.

minCoefficientsUpdate A minimum distance between coefficient vectors in two contiguous iterations. Uses as a condition of convergence in the optimization algorithm. If a difference between the two vectors is small enough, there is no reason to continue fitting. Default value is 1e-12

probabilityThreshold A probability on the basis of which it is decided, whether an observation relates to positive class label (see positiveLabel parameter) or to negative class label (see negativeLabel parameter). The greater the probability, the more strict the classifier is. Default value is 0.5.

lambda A coefficient of regularization. Uses to prevent the regressor's overfitting. The more the value of lambda, the more regular the coefficients of the equation of the predicting hyperplane are. Extremely large lambda may decrease the coefficients to nothing, otherwise too small lambda may be a cause of too large absolute values of the coefficients, that is also bad.

fitIntercept Whether or not to fit intercept term. Default value is true. Intercept in 2-dimensional space is a bias of the line (relative to X-axis).

interceptScale A value, defining a size of the intercept.

initialCoefficients Coefficients to be used in the first iteration of optimization algorithm. initialCoefficients is a vector, length of which must be equal to the number of features in trainingData : in case of logistic regression only one column from trainingData is used as a prediction target column, thus the number of features is equal to the number of columns in trainingData minus 1 (target column). Keep in mind, that if your model considers intercept term, initialCoefficients should contain an extra element in the beginning of the vector and it denotes the intercept term coefficient

positiveLabel A value that will be used for the positive class. By default, 1.

negativeLabel A value that will be used for the negative class. By default, 0.

collectLearningData Whether or not to collect learning data, for instance cost function value per each iteration. Affects performance much. If collectLearningData is true, one may access costPerIteration getter in order to evaluate learning process more thoroughly. Default value is false

dtype A data type for all the numeric values, used by the algorithm. Can affect performance or accuracy of the computations. Default value is DType.float32

Example:

import 'package:ml_algo/ml_algo.dart';
import 'package:ml_dataframe/ml_dataframe.dart';

void main() {
  final samples = getPimaIndiansDiabetesDataFrame().shuffle();
  final model = LogisticRegressor.newton(
    samples,
    'Outcome',
    iterationsLimit: 50,
   );
}

Implementation

factory LogisticRegressor.newton(
  DataFrame trainingData,
  String targetName, {
  int iterationsLimit = iterationLimitDefaultValue,
  double minCoefficientsUpdate = minCoefficientsUpdateDefaultValue,
  double probabilityThreshold = probabilityThresholdDefaultValue,
  double lambda = lambdaDefaultValue,
  bool fitIntercept = fitInterceptDefaultValue,
  double interceptScale = interceptScaleDefaultValue,
  num positiveLabel = positiveLabelDefaultValue,
  num negativeLabel = negativeLabelDefaultValue,
  bool collectLearningData = collectLearningDataDefaultValue,
  DType dtype = dTypeDefaultValue,
  Vector? initialCoefficients,
}) =>
    initLogisticRegressorModule().get<LogisticRegressorFactory>().create(
          trainData: trainingData,
          targetName: targetName,
          optimizerType: LinearOptimizerType.newton,
          iterationsLimit: iterationsLimit,
          initialLearningRate: initialLearningRateDefaultValue,
          decay: decayDefaultValue,
          dropRate: dropRateDefaultValue,
          minCoefficientsUpdate: minCoefficientsUpdate,
          probabilityThreshold: probabilityThreshold,
          lambda: lambda,
          regularizationType: RegularizationType.L2,
          batchSize: trainingData.shape.first,
          fitIntercept: fitIntercept,
          interceptScale: interceptScale,
          isFittingDataNormalized: false,
          learningRateType: defaultLearningRateType,
          initialCoefficientsType: initialCoefficientsTypeDefaultValue,
          initialCoefficients:
              initialCoefficients ?? Vector.empty(dtype: dtype),
          positiveLabel: positiveLabel,
          negativeLabel: negativeLabel,
          collectLearningData: collectLearningData,
          dtype: dtype,
        );