LogisticRegressor class abstract
Logistic regressionbased classification.
Logistic regression is an algorithm that solves the binary classification problem. The algorithm uses maximization of the passed data likelihood. In other words, the regressor iteratively tries to select coefficients that makes combination of passed features and the coefficients most likely.
Constructors
 LogisticRegressor(DataFrame trainData, String targetName, {LinearOptimizerType optimizerType = LinearOptimizerType.newton, int iterationsLimit = iterationLimitDefaultValue, double initialLearningRate = initialLearningRateDefaultValue, double decay = decayDefaultValue, int dropRate = dropRateDefaultValue, double minCoefficientsUpdate = minCoefficientsUpdateDefaultValue, double probabilityThreshold = probabilityThresholdDefaultValue, double lambda = lambdaDefaultValue, int batchSize = batchSizeDefaultValue, bool fitIntercept = fitInterceptDefaultValue, double interceptScale = interceptScaleDefaultValue, bool isFittingDataNormalized = isFittingDataNormalizedDefaultValue, LearningRateType learningRateType = learningRateTypeDefaultValue, InitialCoefficientsType initialCoefficientsType = initialCoefficientsTypeDefaultValue, num positiveLabel = positiveLabelDefaultValue, num negativeLabel = negativeLabelDefaultValue, bool collectLearningData = collectLearningDataDefaultValue, DType dtype = dTypeDefaultValue, RegularizationType? regularizationType, Vector? initialCoefficients, int? randomSeed})

Parameters:
factory
 LogisticRegressor.BGD(DataFrame trainingData, String targetName, {required LearningRateType learningRateType, int iterationsLimit = iterationLimitDefaultValue, double initialLearningRate = initialLearningRateDefaultValue, double decay = decayDefaultValue, int dropRate = dropRateDefaultValue, double minCoefficientsUpdate = minCoefficientsUpdateDefaultValue, double probabilityThreshold = probabilityThresholdDefaultValue, double lambda = lambdaDefaultValue, bool fitIntercept = fitInterceptDefaultValue, double interceptScale = interceptScaleDefaultValue, InitialCoefficientsType initialCoefficientsType = initialCoefficientsTypeDefaultValue, num positiveLabel = positiveLabelDefaultValue, num negativeLabel = negativeLabelDefaultValue, bool collectLearningData = collectLearningDataDefaultValue, DType dtype = dTypeDefaultValue, Vector? initialCoefficients})

Creates a LogisticRegressor instance based on Batch Gradient Descent
algorithm
factory
 LogisticRegressor.fromJson(String json)

Restores previously fitted classifier instance from the
json
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})

Creates a LogisticRegressor instance based on NewtonRaphson method
factory
 LogisticRegressor.SGD(DataFrame trainingData, String targetName, {required LearningRateType learningRateType, int iterationsLimit = iterationLimitDefaultValue, double initialLearningRate = initialLearningRateDefaultValue, double decay = decayDefaultValue, int dropRate = dropRateDefaultValue, double minCoefficientsUpdate = minCoefficientsUpdateDefaultValue, double probabilityThreshold = probabilityThresholdDefaultValue, double lambda = lambdaDefaultValue, bool fitIntercept = fitInterceptDefaultValue, double interceptScale = interceptScaleDefaultValue, InitialCoefficientsType initialCoefficientsType = initialCoefficientsTypeDefaultValue, num positiveLabel = positiveLabelDefaultValue, num negativeLabel = negativeLabelDefaultValue, bool collectLearningData = collectLearningDataDefaultValue, DType dtype = dTypeDefaultValue, Vector? initialCoefficients, int? seed})

Creates a LogisticRegressor instance based on Stochastic
Gradient Descent algorithm
factory
Properties
 batchSize → int

A size of data (in rows) that was used in a single iteration of
coefficients learning process.
no setter
 coefficientsByClasses → Matrix

A matrix, where each column is a vector of coefficients, associated with
the specific class
no setterinherited

costPerIteration
→ List<
num> ? 
Returns a list of cost values per each learning iteration. Returns null
if the parameter
collectLearningData
of the default constructor is falseno setter  decay → double

A value that was used for the learning rate decay
no setter
 dropRate → int

A value that was used for the learning rate drop rate
no setter
 dtype → DType

A type for all numeric values using by the
Predictor
no setterinherited  fitIntercept → bool

A flag denoting whether the intercept term is considered during
learning of the classifier or not
no setterinherited
 hashCode → int

The hash code for this object.
no setterinherited
 initialCoefficients → Vector?

Coefficients which were used at the very first model's coefficients
learning algorithm iteration.
no setter
 initialCoefficientsType → InitialCoefficientsType

A coefficient set type that was used by the chosen optimizer at the very
first iteration of coefficients learning algorithm.
no setter
 initialLearningRate → double

Initial learning rate value of chosen optimization algorithm
no setter
 interceptScale → num

A value defining a size of the intercept if fitIntercept is
true
no setterinherited  isFittingDataNormalized → bool

Whether the fitting data was normalized or not prior to the model's
coefficients learning
no setter
 iterationsLimit → int

A number of fitting iterations that was used to learn the model's
coefficients.
no setter
 lambda → double

A coefficient of regularization
no setter
 learningRateType → LearningRateType

A type of a learning rate behaviour update strategy.
no setter
 linkFunction → LinkFunction

A function that is used for converting learned coefficients into
probabilities
no setterinherited
 minCoefficientsUpdate → double

A minimum distance between coefficient vectors in
two contiguous iterations which was used to learn the model's
coefficients.
no setter
 negativeLabel → num

A value using to encode negative class.
no setterinherited
 optimizerType → LinearOptimizerType

An algorithm of linear optimization that was used
to find the best coefficients of loglikelihood cost function. Also
shows which regularization type (L1 or L2) was used to learn the model's
coefficients.
no setter
 positiveLabel → num

A value using to encode positive class.
no setterinherited
 probabilityThreshold → num

A probability, on the basis of which it is decided,
whether an observation relates to a positive class label (see
positiveLabel parameter) or to a negative class label (see negativeLabel
parameter)
no setter
 randomSeed → int?

A seed that was passed to a random value generator used by a stochastic
optimizer.
no setter
 regularizationType → RegularizationType?

A way the coefficients of the classification were regularized during the
model's coefficients learning process to prevent model overfitting.
no setter
 runtimeType → Type

A representation of the runtime type of the object.
no setterinherited
 schemaVersion → int?

Contains a version of the current json schema
no setterinherited

targetNames
→ Iterable<
String> 
A collection of target column names of a dataset which was used to learn the ML
model
no setterinherited
Methods

assess(
DataFrame observations, MetricType metricType) → double 
Assesses model performance according to provided
metricType
inherited 
noSuchMethod(
Invocation invocation) → dynamic 
Invoked when a nonexistent method or property is accessed.
inherited

predict(
DataFrame testFeatures) → DataFrame 
Returns prediction, based on the learned coefficients
inherited

predictProbabilities(
DataFrame testFeatures) → DataFrame 
Returns predicted distribution of probabilities for each observation in
the passed
testFeatures
inherited 
retrain(
DataFrame data) → LogisticRegressor 
Reruns the learning process on the new training
data
. The features, model algorithm, and hyperparameters remain the same.inherited 
saveAsJson(
String filePath) → Future< File> 
Saves a jsonserializable map into a newly created file with the path
filePath
inherited 
toJson(
) → Map< String, dynamic> 
Returns a jsonserializable map
inherited

toString(
) → String 
A string representation of this object.
inherited
Operators

operator ==(
Object other) → bool 
The equality operator.
inherited