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 Newton-Raphson 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 false
no 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
Re-runs 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 json-serializable map into a newly created file with the path filePath
inherited
toJson() → Map<String, dynamic>
Returns a json-serializable map
inherited
toString() → String
A string representation of this object.
inherited

Operators

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