hpipy.extensions.RandomForestModel

class hpipy.extensions.RandomForestModel(hpi_data, **kwargs)

Bases: BaseHousePriceModel

Random forest house price model.

Methods

__init__	Initialize base house price model.
fit	Fit the random forest model and generate index coefficients.

Attributes

coefficients
model_obj
periods
base_price
params

Parameters:

• hpi_data (TransactionData)

• kwargs (Any)

fit(dep_var=None, ind_var=None, estimator='pdp', log_dep=True, n_estimators=100, quantile=None, random_seed=0, **kwargs)

Fit the random forest model and generate index coefficients.

Parameters:

• dep_var (str | None, optional) – Dependent variable. Defaults to None.

• ind_var (list[str] | None, optional) – Independent variable(s). Defaults to None.

• estimator (str, optional) – Estimator type. Defaults to “pdp”.

• log_dep (bool, optional) – Log transform the dependent variable. Defaults to True.

• n_estimators (int, optional) – Number of estimators. Defaults to 100.

• quantile (float | None, optional) – Quantile to compute. Defaults to None.

• random_seed (int, optional) – Random seed to use. Defaults to 0.

• kwargs (Any)

Returns:

Fitted model.

Return type:

Self

__init__(hpi_data, **kwargs)

Initialize base house price model.

Parameters:

• hpi_data (TransactionData)

• kwargs (Any)

Return type:

None

coefficients: DataFrame

model_obj: Any

periods: DataFrame

base_price: float

params: dict[str, Any]