ml_algo

• LogisticRegressor, SoftmaxRegressor: redundant link function implementations removed

• DecisionTreeTrainer: redundant helper for trainer creation removed

• xrange 1.0.0 supported

• ml_dataframe 0.4.0 supported
• README.md: example for flutter developers corrected

• More strict analyser options added

• README.md: example for flutter developers added

Models retraining functionality added (#169)

Hyperparameters added to models interfaces (#168)

• DTypeJsonConverter added
• MatrixJsonConverter added
• VectorJsonConverter added
• DistanceTypeJsonConverter added

KNN models: serialization/deserialization added (#166)

• ml_dataframe: version 0.3.0 supported
• README.md: build badge corrected

• Github actions set up

• DI logic:
  • conditional dependency registering added

• Awfully long identifier SequenceElementsDistributionCalculator renamed to DistributionCalculator

• README:
  • typos corrected
  • LogisticRegressor example corrected

• RSS metric added

• Documentation for classification metrics improved

• Documentation for RMSE metric improved

• Documentation for MAPE metric improved

• classificationMetrics constant list added
• regressionMetrics constant list added

• Recall metric added

• MAPE metric: output range squeezed to [0, 1]

• RegressorAssessor: unit tests added

precision metric added (#143)

• injector lib 1.0.9 supported

• pubspec:
  • injector dependency corrected

• README:
  • File path note for flutter developers added

• README:
  • Kfold constructor renamed to kFold
  • brackets removed from LogisticRegressor constructor arguments
  • file path note added

ml_dataframe 0.2.0 supported (#142)

README: Examples on prediction and collecting learning data added (#141)

collectLearningData argument added to SoftmaxRegressor default constr…

README: Advanced usage example on Logistic regression (#139)

splitData helper added (#138)

Rename and reorganise data splitters (#137)

• Breaking change:
  • CrossValidator: evalute method's api changed, it returns a Future resolving with scores Vector now instead of a double value

• LinearRegressor:
  • Default constructor: collectLearningData parameter added

• LogisticRegressor:
  • Default constructor: collectLearningData parameter added

• ml_dataframe dependency updated
• xrange dependency constrain removed

• LinkFunction:
  • Float64InverseLogitLinkFunction added
  • Float64SoftmaxLinkFunction added

• LinearRegressor: serialization/deserialization functionality added with possibility to save the model into a file as json

• SoftmaxRegressor: serialization/deserialization functionality added with possibility to save the model into a file as json

• DecisionTreeClassifier: documentation added for fromJson constructor

• LogisticRegressor: serialization/deserialization functionality added with possibility to save the model into a file as json

• DecisionTreeClassifier: serialization/deserialization functionality added with possibility to save the model into a file as json

• TreeLeafLabel: probability validation improvements

• DecisionTreeClassifier: classifier instantiating refactored
• TreeSolver: DI support added

• SoftmaxRegressor: classifier instantiating refactored

• LogisticRegressor: classifier instantiating refactored

• KnnClassifierImpl: unit tests for predictProbability method added

• KnnClassifier: classifier instantiating refactored

• readme: KnnRegressor usage example fixed

• KnnClassifier class added

• KNN algorithm: standardization for distance added
• KnnRegressor:
  • default kernel changed to gaussian
  • k parameter is required now

• KNN regression: documentation for kernel function types added
• KnnRegressor: finding weighted average using kernel function fixed

• CrossValidator: onDataSplit hook added

• Predictor's API: DataFrame used instead of Matrix
• DecisionTreeSolver: data splitting logic fixed

• xrange package version locked