gwlearn.linear_model.GWLogisticRegression

class gwlearn.linear_model.GWLogisticRegression(bandwidth, fixed=False, kernel='bisquare', n_jobs=-1, fit_global_model=True, measure_performance=True, strict=False, keep_models=False, temp_folder=None, batch_size=None, **kwargs)

__init__(bandwidth, fixed=False, kernel='bisquare', n_jobs=-1, fit_global_model=True, measure_performance=True, strict=False, keep_models=False, temp_folder=None, batch_size=None, **kwargs)

Methods

__init__(bandwidth[, fixed, kernel, n_jobs, ...])
fit(X, y, geometry)	Fit the geographically weighted model
predict(X, geometry)
predict_proba(X, geometry)	Predict probabiliies using the ensemble of local models

fit(X, y, geometry)

Fit the geographically weighted model

Parameters:

Xpd.DataFrame

Independent variables

ypd.Series

Dependent variable

geometrygpd.GeoSeries

Geographic location