Package simpleml.model.supervised.classification

April 22, 2022 ยท View on GitHub

Tutorial - Idea and basic concepts | Interface | API | DSL

Table of Contents

Class DecisionTreeClassifier

Functionalities to train a decision tree classification model.

Constructor parameters:

  • maxDepth: Int? = null - No description available.

Attributes:

  • attr maxDepth: Int? - No description available.

fit (Instance Method )

Train the model given a dataset of features and a dataset of labels

Parameters:

  • features: Dataset - No description available.
  • target: Dataset - No description available.

Results:

  • trainedModel: DecisionTreeClassifierModel - No description available.

Class DecisionTreeClassifierModel

A trained decision tree classification model.

Constructor parameters: None expected.

predict (Instance Method )

Predict values given a dataset of features

Parameters:

  • features: Dataset - A dataset consisting of features for prediction.

Results:

  • results: Dataset - A dataset consisting of the predicted values.

Class RandomForestClassifier

Functionalities to train a random forest classification model.

Constructor parameters:

  • nEstimator: Int = 100 - No description available.
  • criterion: String = "gini" - No description available.
  • maxDepth: Int? = null - No description available.
  • randomState: Int? = null - No description available.

Attributes:

  • attr criterion: String - No description available.
  • attr maxDepth: Int? - No description available.
  • attr nEstimator: Int - No description available.
  • attr randomState: Int? - No description available.

fit (Instance Method )

Train the model given a dataset of features and a dataset of labels

Parameters:

  • features: Dataset - No description available.
  • target: Dataset - No description available.

Results:

  • trainedModel: RandomForestClassifierModel - No description available.

Class RandomForestClassifierModel

A trained random forest classification model.

Constructor parameters: None expected.

predict (Instance Method )

Predict values given a dataset of features

Parameters:

  • features: Dataset - A dataset consisting of features for prediction.

Results:

  • results: Dataset - A dataset consisting of the predicted values.

Class SupportVectorMachineClassifier

Functionalities to train an SVM classification model.

Constructor parameters:

  • penalty: String = "l2" - No description available.
  • loss: String = "squared_hinge" - No description available.
  • dual: Boolean = true - No description available.
  • tol: Float = 1e-4 - No description available.
  • c: Float = 1.0 - No description available.
  • multiClass: String = "ovr" - No description available.

Attributes:

  • attr c: Float - No description available.
  • attr dual: Boolean - No description available.
  • attr loss: String - No description available.
  • attr multiClass: String - No description available.
  • attr penalty: String - No description available.
  • attr tol: Float - No description available.

fit (Instance Method )

Train the model given a dataset of features and a dataset of labels

Parameters:

  • features: Dataset - No description available.
  • target: Dataset - No description available.

Results:

  • trainedModel: SupportVectorMachineClassifierModel - No description available.

Class SupportVectorMachineClassifierModel

A trained SVM classification model.

Constructor parameters: None expected.

predict (Instance Method )

Predict values given a dataset of features

Parameters:

  • features: Dataset - A dataset consisting of features for prediction.

Results:

  • results: Dataset - A dataset consisting of the predicted values.

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