python - 在 scikit learn 中预测多个值作为模型结果

Question

我使用 scikit learn 算法创建了一个模型。

rf = RandomForestClassifier(n_estimators = 10,random_state=seed)
rf.fit(X_train,Y_train)

shift_id=2099.0
user_id=1402.0
status=['S']
shift_organisation_id=15.0
shift_department_id=20.0
open_positions=71.0
city=['taunton']
role_id=3.0
specialty_id=16.0
years_of_experience=10.0
nurse_zip=2780.0
shifts_zip=2021.0

status = status_encoder.transform(status)
city = city_encoder.transform(city)

X = np.array([shift_id, user_id, status, shift_organisation_id, shift_department_id, open_positions, city, role_id, specialty_id, years_of_experience, nurse_zip, shifts_zip])
location_id = rf.predict(X.reshape(1,-1))
print(location_id)

给出这样的结果

[25]

据我了解，25 是该模型的最佳预测值。但我想得到最好的 3 个值作为结果。我怎样才能得到它？

在这种情况下，预测结果会像

[23,45,25]

Answer 1

您可以使用 predict_proba 方法返回类概率并从中获取前 3 个值 ref

rf = RandomForestClassifier(n_estimators = 10,random_state=seed)
rf.fit(X_train,Y_train)

shift_id=2099.0
user_id=1402.0
status=['S']
shift_organisation_id=15.0
shift_department_id=20.0
open_positions=71.0
city=['taunton']
role_id=3.0
specialty_id=16.0
years_of_experience=10.0
nurse_zip=2780.0
shifts_zip=2021.0

status = status_encoder.transform(status)
city = city_encoder.transform(city)

X = np.array([shift_id, user_id, status, shift_organisation_id, shift_department_id, open_positions, city, role_id, specialty_id, years_of_experience, nurse_zip, shifts_zip])
location_id = rf.predict_proba(X.reshape(1,-1))
print(location_id)