Python 如何解决XGBoost错误；功能“名称不匹配”；？

我正在研究使用XGBoost来使用iris数据集进行预测。该模型经过训练，但当我尝试进行新的预测时，我得到了以下错误：ValueError:feature\u names mismatch

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris

iris = load_iris()
iris_df = pd.DataFrame(data= np.c_[iris['data'], iris['target']],
                 columns= iris['feature_names'] + ['target'])

iris_df.rename(columns={'sepal length (cm)':'sepal_length'}, inplace=True)
iris_df.rename(columns={'sepal width (cm)':'sepal_width'},inplace=True)
iris_df.rename(columns={'petal length (cm)':'petal_length'},inplace=True)
iris_df.rename(columns={'petal width (cm)':'petal_width'}, inplace=True)

data = iris_df[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']]
target = iris_df[['targets']]

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=0)

import xgboost as xgb

train = xgb.DMatrix(X_train, label=y_train)
test = xgb.DMatrix(X_test, label=y_test)

param = {
    'max_depth':4,
    'eta':0.3,
    'objective': 'multi:softmax',
    'num_class': 3}
epochs = 10

model = xgb.train(param, train, epochs)

predictions = model.predict(test)
print(predictions)

from sklearn.metrics import accuracy_score

accuracy_score(y_test, predictions)

上面的所有代码都可以工作，但做出新的预测会抛出一个错误：

testArray=np.array([[5.1,3.5,1.4,0.2]])

test_individual=xgb.DMatrix(testArray)

print(model.predict(test_individual))\

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如何使新的预测工作？

将数据数组“testArray”和列标签的标签列表传递给xgb.DMatrix：

test_individual = xgb.Dmatrix(testArray, label = X_train.columns)

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将一个一维数组传递给xgb.DMatrix：

testArray = np.array([5.1,3.5,1.4,0.2])

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解决了这个问题。必须将数据帧对象转换为数组：

train = iris_df[['sepal_length','sepal_width','petal_length','petal_width']].values
test = iris_df[['target']].values

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获取了以下错误：“ValueError:（'Expecting 2D numpy.ndarray，Get:'，（4，）”获取了以下错误：“ValueError:（'Expecting 2D numpy.ndarray，Get:'，（4，）”为什么要调用

iris_df[['target']）。值作为测试？