对多个模型使用装袋算法

Question

我正在尝试为 LasVegasTripAdvisorReviews-Dataset 建立模型使用套袋算法，我有一个错误（不支持多标签和多输出分类）你能帮我告诉我如何解决这个错误吗)

问候
附件包含 link 到 lasvegas dataset LasVegasTripAdvisorReviews-Dataset

# Voting Ensemble for Classification
import pandas
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression   

from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.ensemble import VotingClassifier,GradientBoostingClassifier,AdaBoostClassifier,RandomForestClassifier

url = "h:/LasVegasTripAdvisorReviews-Dataset.csv"
names = ['User country','Nr. reviews','Nr. hotel reviews','Helpful votes','Period of stay','Traveler type','Pool','Gym','Tennis court','Spa','Casino','Free internet','Hotel name','Hotel stars','Nr. rooms','User continent','Member years','Review month','Review weekday','Score']
dataframe = pandas.read_csv(url, names=names)
array = dataframe.values
X = array[:,:]
Y = array[:,:]
seed = 7
kfold = model_selection.KFold(n_splits=10, random_state=seed)
# create the sub models
estimators = []
model1 = AdaBoostClassifier()
estimators.append(('AdaBoost', model1))
model2 = GradientBoostingClassifier()
estimators.append(('GradientBoosting', model2))
model3 = RandomForestClassifier()
estimators.append(('RandomForest', model3))
# create the ensemble model
ensemble = VotingClassifier(estimators)
results = model_selection.cross_val_score(ensemble, X, Y, cv=kfold)
print(results.mean())

堆栈跟踪：

NotImplementedError                       Traceback (most recent call last)
<ipython-input-9-bda887b4022f> in <module>
     27 # create the ensemble model
     28 ensemble = VotingClassifier(estimators)
---> 29 results = model_selection.cross_val_score(ensemble, X, Y, cv=kfold)
     30 print(results.mean())

/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_validation.py in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)
    400                                 fit_params=fit_params,
    401                                 pre_dispatch=pre_dispatch,
--> 402                                 error_score=error_score)
    403     return cv_results['test_score']
    404 
...
...
NotImplementedError: Multilabel and multi-output classification is not supported.

Answer 1

你有这行：

    X = array[:,:]
    Y = array[:,:]

意味着你的特征矩阵（X）和目标向量（Y）是相同的。

您只需要选择一列作为您的Y。

例如，假设您希望 最后一列 为 Y.

然后，您应该将上面的行更改为：

X = values[:,:-1]
Y = values[:,-1:]

这应该可以解决您遇到的错误。你的错误基本上意味着：I don't support more than one column in Y.

对多个模型使用装袋算法

using bagging algorithm with multiple models

python

machine-learning

ensemble-learning