Package simpleml.model.supervised.regression

April 22, 2022 ยท View on GitHub

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

Table of Contents

Class DecisionTreeRegressor

Functionalities to train a decision tree regression 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 - A dataset consisting of features for training the model.
  • target: Dataset - A dataset consisting of one column with the target values.

Results:

  • trainedModel: DecisionTreeRegressorModel - A trained decision tree regression model.

Class DecisionTreeRegressorModel

A trained decision tree regression 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 LinearRegression

Functionalities to train a linear regression model.

Constructor parameters: None expected.

fit (Instance Method )

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

Parameters:

  • features: Dataset - A dataset consisting of features for training the model.
  • target: Dataset - A dataset consisting of one column with the target values.

Results:

  • trainedModel: LinearRegressionModel - A trained linear regression model.

Class LinearRegressionModel

A trained linear regression 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 RandomForestRegressor

Functionalities to train a random forest regression model.

Constructor parameters:

  • nEstimator: Int = 100 - No description available.
  • criterion: String = "mse" - 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: RandomForestRegressorModel - No description available.

Class RandomForestRegressorModel

A trained random forest regression 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 RidgeRegression

Functionalities to train a ridge regression model.

Constructor parameters:

  • regularizationStrength: Float = 0.5 - No description available.

Attributes:

  • attr regularizationStrength: 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: RidgeRegressionModel - No description available.

Class RidgeRegressionModel

A trained ridge regression 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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