LinearRegressor class abstract
Linear regression
A typical linear regressor uses the equation of a line for multidimensional
space to make a prediction. Each x
in the equation has its own dedicated
coefficient (weight) and the combination of these x
es and its dedicated
coefficients gives the y
term (outcome). The latter is the value that the
regressor should predict, and since all the x
values are known (they
are the input for the algorithm), the regressor should find the best
coefficients (weights) for each x
es to make a best prediction of y
term.
Constructors
 LinearRegressor(DataFrame fittingData, String targetName, {LinearOptimizerType optimizerType = LinearOptimizerType.closedForm, int iterationsLimit = iterationLimitDefaultValue, LearningRateType learningRateType = learningRateTypeDefaultValue, InitialCoefficientsType initialCoefficientsType = initialCoefficientsTypeDefaultValue, double initialLearningRate = initialLearningRateDefaultValue, double decay = decayDefaultValue, int dropRate = dropRateDefaultValue, double minCoefficientsUpdate = minCoefficientsUpdateDefaultValue, double lambda = lambdaDefaultValue, bool fitIntercept = fitInterceptDefaultValue, double interceptScale = interceptScaleDefaultValue, int batchSize = batchSizeDefaultValue, bool isFittingDataNormalized = isFittingDataNormalizedDefaultValue, bool collectLearningData = collectLearningDataDefaultValue, DType dtype = dTypeDefaultValue, RegularizationType? regularizationType, int? randomSeed, Matrix? initialCoefficients})

Parameters:
factory
 LinearRegressor.fromJson(String json)

Restores previously fitted LinearRegressor instance from the
json
factory  LinearRegressor.lasso(DataFrame trainData, String targetName, {int iterationLimit = iterationLimitDefaultValue, InitialCoefficientsType initialCoefficientType = initialCoefficientsTypeDefaultValue, double minCoefficientUpdate = minCoefficientsUpdateDefaultValue, double lambda = lambdaDefaultValue, bool fitIntercept = fitInterceptDefaultValue, double interceptScale = interceptScaleDefaultValue, bool isDataNormalized = isFittingDataNormalizedDefaultValue, bool collectLearningData = collectLearningDataDefaultValue, DType dtype = dTypeDefaultValue, Matrix? initialCoefficients})

Lasso regression
factory
 LinearRegressor.newton(DataFrame trainData, String targetName, {double lambda = lambdaDefaultValue, bool fitIntercept = fitInterceptDefaultValue, double interceptScale = interceptScaleDefaultValue, bool collectLearningData = collectLearningDataDefaultValue, DType dtype = dTypeDefaultValue, Matrix? initialCoefficients})

Linear regression with NewtonRaphson optimization and L2 regularization
factory
 LinearRegressor.SGD(DataFrame trainData, String targetName, {int iterationLimit = iterationLimitDefaultValue, LearningRateType learningRateType = learningRateTypeDefaultValue, InitialCoefficientsType initialCoefficientType = initialCoefficientsTypeDefaultValue, double initialLearningRate = initialLearningRateDefaultValue, double decay = decayDefaultValue, int dropRate = dropRateDefaultValue, double minCoefficientUpdate = minCoefficientsUpdateDefaultValue, double lambda = lambdaDefaultValue, bool fitIntercept = fitInterceptDefaultValue, double interceptScale = interceptScaleDefaultValue, bool collectLearningData = collectLearningDataDefaultValue, DType dtype = dTypeDefaultValue, int? randomSeed, Matrix? initialCoefficients})

Linear regression with Stochastic Gradient Descent optimization and L2
regularization
factory
Properties
 batchSize → int

A size of a batch of data that was used in a single iteration of the
optimization algorithm
no setter
 coefficients → Vector

Learned coefficients (or weights) for given features
no setter

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 → num

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 regressor or not
no setter
 hashCode → int

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

Coefficients that were used at the very first optimization iteration
during the model's coefficients learning stage
no setter
 initialCoefficientsType → InitialCoefficientsType

Coefficients generator type that was used at the very first optimization
iteration during the model's coefficients learning
no setter
 initialLearningRate → num

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 setter  isFittingDataNormalized → bool

Was the train data normalized or not prior to the model's coefficients
learning stage
no setter
 iterationsLimit → int

A maximum number of optimization iterations that was used
during model's coefficients learning
no setter
 lambda → num

A regularization value that was used to prevent overfitting of the model
no setter
 learningRateType → LearningRateType

Learning rate update strategy that was used to learn the model's
coefficients
no setter
 minCoefficientsUpdate → num

A coefficients update value that was used as a stop criteria during the
model's coefficients learning process
no setter
 optimizerType → LinearOptimizerType

Optimization algorithm that was used to learn the model's coefficients
no setter
 randomSeed → int?

A value that was used during the model's coefficients learning stage to
init the randomizer for a stochastic optimizer (if the latter was chosen
to learn the model's coefficients)
no setter
 regularizationType → RegularizationType?

A regularization strategy that was used to prevent overfitting of the
model
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
 targetName → String

A string that serves as a name of the target column containing
observation labels. Uses in a predicted dataframe returning from predict
method
no setter

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

retrain(
DataFrame data) → LinearRegressor 
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