`flexcode.regression_models`

Module Contents

Classes

`FlexCodeRegression`
`NN`
`RandomForest`
`XGBoost`
`Lasso`
`CustomModel`

Attributes

`XGBOOST_AVAILABLE`
`SKLEARN_AVAILABLE`

XGBOOST_AVAILABLE = True[source]

SKLEARN_AVAILABLE = True[source]

class FlexCodeRegression(max_basis)[source]

Bases: object

fit(x_train, z_basis, weight)[source]

predict(x_new)[source]

class NN(max_basis, params, *args, **kwargs)[source]

Bases: FlexCodeRegression

fit(x_train, z_basis, weight)[source]

cv_optim(x_train, z_basis)[source]

predict(x_test)[source]

class RandomForest(max_basis, params, *args, **kwargs)[source]

Bases: FlexCodeRegression

fit(x_train, z_basis, weight=None)[source]

cv_optim(x_train, z_basis, weight=None)[source]

predict(x_test)[source]

class XGBoost(max_basis, params, *args, **kwargs)[source]

Bases: FlexCodeRegression

fit(x_train, z_basis, weight=None)[source]

cv_optim(x_train, z_basis, weight=None)[source]

predict(x_test)[source]

class Lasso(max_basis, params, *args, **kwargs)[source]

Bases: FlexCodeRegression

fit(x_train, z_basis, weight=None)[source]

cv_optim(x_train, z_basis)[source]

predict(x_test)[source]

class CustomModel(max_basis, params, custom_model, *args, **kwargs)[source]

Bases: FlexCodeRegression

fit(x_train, z_basis, weight=None)[source]

cv_optim(x_train, z_basis)[source]

predict(x_test)[source]