terrasensetk.algorithms.Regression package

Submodules

terrasensetk.algorithms.Regression.GradientBoostingRegressor module

class terrasensetk.algorithms.Regression.GradientBoostingRegressor.GradientBoostingRegressor(args={}, **kwargs)[source]

Bases: terrasensetk.algorithms.IAlgorithm.IAlgorithm

Implementation of the GradientBoostingRegressor from the scikitlearn library

clone()[source]
fit(x_values, y_values, *args)[source]
get_model()[source]
get_params()[source]
objective_function(trial, x_train, y_train, x_test, y_test)[source]
predict(x_values)[source]
set_params(params)[source]

terrasensetk.algorithms.Regression.MLPRegressor module

class terrasensetk.algorithms.Regression.MLPRegressor.MLPRegressor(args={}, **kwargs)[source]

Bases: terrasensetk.algorithms.IAlgorithm.IAlgorithm

Implementation of the MultiLayerPerceptronRegressor from the scikitlearn library

clone()[source]
fit(x_values, y_values, *args)[source]
get_model()[source]
get_params()[source]
objective_function(trial, x_train, y_train, x_test, y_test)[source]
predict(x_values)[source]
set_params(params)[source]

terrasensetk.algorithms.Regression.PLSRegressor module

class terrasensetk.algorithms.Regression.PLSRegressor.PLSRegressor(args={}, **kwargs)[source]

Bases: terrasensetk.algorithms.IAlgorithm.IAlgorithm

Implementation of the PartialLeastSquaresRegressor from the scikitlearn library

clone()[source]
fit(x_values, y_values, *args)[source]
get_model()[source]
get_params()[source]
objective_function(trial, x_train, y_train, x_test, y_test)[source]
predict(x_values)[source]
set_params(params)[source]

terrasensetk.algorithms.Regression.RandomForestRegressor module

class terrasensetk.algorithms.Regression.RandomForestRegressor.RandomForestRegressor(args={}, **kwargs)[source]

Bases: terrasensetk.algorithms.IAlgorithm.IAlgorithm

Implementation of the RandomForestRegressor from the scikitlearn library

clone()[source]
fit(x_values, y_values, *args)[source]
get_model()[source]
get_params()[source]
objective_function(trial, x_train, y_train, x_test, y_test)[source]
predict(x_values)[source]
set_params(params)[source]

terrasensetk.algorithms.Regression.SupportVectorRegression module

class terrasensetk.algorithms.Regression.SupportVectorRegression.SupportVectorRegression(args={}, **kwargs)[source]

Bases: terrasensetk.algorithms.IAlgorithm.IAlgorithm

Implementation of the SupportVectorRegression from the scikitlearn library

clone()[source]
fit(x_values, y_values, *args)[source]
get_model()[source]
get_params()[source]
objective_function(trial, x_train, y_train, x_test, y_test)[source]
predict(x_values)[source]
set_params(params)[source]

Module contents