У меня есть следующий код
xgb = XGBRegressor(booster='gblinear', reg_lambda=0, learning_rate=0.028)
print(xgb)
xgb.fit(X_train_sc, y_train)
y_pred = xgb.predict(X_test_sc)
print("\nFeature Importances:")
for item in zip(feature_list_transform, xgb.feature_importances_):
print("{1:10.4f} - {0}".format(item[0],item[1]))
print("\nR-squared, training set:")
print(xgb.score(X_train_sc,y_train))
print("R-squared, test set:")
print(xgb.score(X_test_sc,y_test))
print("\nRoot-mean squared error, from metrics:")
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
print(rmse)
Вывод:
XGBRegressor(base_score=0.5, booster='gblinear', colsample_bylevel=1,
colsample_bytree=1, gamma=0, learning_rate=0.028, max_delta_step=0,
max_depth=3, min_child_weight=1, missing=None, n_estimators=100,
n_jobs=1, nthread=None, objective='reg:linear', random_state=0,
reg_alpha=0, reg_lambda=0, scale_pos_weight=1, seed=None,
silent=True, subsample=1)
Feature Importances:
nan - fertility_rate_log
nan - life_expectancy_log
nan - avg_supply_of_protein_of_animal_origin_log
nan - access_to_improved_sanitation_log
nan - access_to_improved_water_sources_log
nan - obesity_prevalence_log
nan - open_defecation_log
nan - access_to_electricity_log
nan - cereal_yield_log
nan - population_growth_log
nan - avg_value_of_food_production_log
nan - gross_domestic_product_per_capita_ppp_log
nan - net_oda_received_percent_gni_log
nan - adult_literacy_rate
nan - school_enrollment_rate_female
nan - school_enrollment_rate_total
nan - caloric_energy_from_cereals_roots_tubers
nan - anemia_prevalence
nan - political_stability
R-squared, training set:
0.5364714955219572
R-squared, test set:
0.714197620258952
Root-mean squared error, from metrics:
5.248174086768801
И ошибка:
c: \ python36 \ lib \ site-packages \ xgboost \ sklearn.py: 420: время выполнения предупреждения:
встречено неверное значение в true_divide return all_features /
all_features.sum ()
Как исправить это Нэн и получить коэффициенты? Наконец, модель работает хорошо.