单例数组array（'data.mean'，dtype ='<U9'）不能被视为有效集合

Question

发生以下错误：

  

Singleton array array('data.mean', dtype='<U9') cannot be considered a valid collection。

我想用参数“ mean”预测天气预报。参数“平均值”是一个十进制数字值。

     import numpy as np
     import pandas as pd
     from sklearn.model_selection import train_test_split
     from sklearn import preprocessing
     from sklearn.ensemble import RandomForestRegressor
     from sklearn.pipeline import make_pipeline
     from sklearn.model_selection import GridSearchCV
     from sklearn.metrics import mean_squared_error, r2_score

     data = pd.read_csv('dissertation_data.csv')
     print (data.describe())
     y = np.array('data.mean')
     X = np.array(data.drop('mean', axis=1))
     X_train, X_test, y_training, y_test = train_test_split(X, y, 
                                                test_size=0.2, 
                                                random_state=123, 
                                                stratify=y)
     pipeline = make_pipeline(preprocessing.StandardScaler(), 
                     RandomForestRegressor(n_estimators=100))
     X_train_scaled = preprocessing.scale(X_train)
     print (X_train_scaled)

     scaler = preprocessing.StandardScaler().fit(X_train)
     X_train_scaled = scaler.transform(X_train)
     X_test_scaled = scaler.transform(X_test)
     hyperparameters = { 'randomforestregressor__max_features' : ['auto', 'sqrt', 'log2'],
              'randomforestregressor__max_depth': [None, 5, 3, 1]}
     clf = GridSearchCV(pipeline, hyperparameters, cv=10)
     clf.fit(X_train, y_training)
     print (clf.best_params_)
     print (clf.refit)
     y_pred = clf.predict(X_test)
     print (r2_score(y_test, y_pred))
     print (mean_squared_error(y_test, y_pred))