Python Pycebox IcePlot在Xgboost上不工作,而在随机林上工作
当我使用XGBoost运行Pycebox时出现以下错误,培训运行得非常完美,但不确定使用iceplot时[fx]字段出现的原因。我还要再次确认它们不在数据集中Python Pycebox IcePlot在Xgboost上不工作,而在随机林上工作,python,machine-learning,scikit-learn,xgboost,Python,Machine Learning,Scikit Learn,Xgboost,当我使用XGBoost运行Pycebox时出现以下错误,培训运行得非常完美,但不确定使用iceplot时[fx]字段出现的原因。我还要再次确认它们不在数据集中 ValueError: feature_names mismatch: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'] ['f0', 'f1', 'f2', 'f3'] expected petal width (cm),
ValueError: feature_names mismatch: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'] ['f0', 'f1', 'f2', 'f3']
expected petal width (cm), petal length (cm), sepal length (cm), sepal width (cm) in input data
training data did not have the following fields: ***f3, f1, f0, f2***
我创建了一个使用iris数据的示例
XGboost代码:
from sklearn.datasets import load_iris
from pycebox.ice import ice, ice_plot
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
import xgboost as xgb
import matplotlib.pyplot as plt
iris = load_iris()
data1 = pd.DataFrame(data= np.c_[iris['data'], iris['target']],
columns= iris['feature_names'] + ['target'])
target = data1['target']
training = data1.drop(['target'],axis=1)
X_train, X_test, y_train, y_test = train_test_split(training, target, test_size=0.4)
xg_reg = xgb.XGBRegressor(random_state=1234,eval_metric='rmse',n_jobs=-1)
xg_reg.fit(X_train,y_train)
forty_ice_df = ice(data=X_train, column='petal length (cm)',
predict=xg_reg.predict)
ice_plot(forty_ice_df, c='dimgray', linewidth=0.3)
plt.ylabel('Pred. Target')
plt.xlabel('petal length (cm)')
当它在随机森林上工作时
rf = RandomForestRegressor(random_state = 1234, n_jobs=18)
rf.fit(X_train, y_train)
forty_ice_df = ice(data=X_train, column='petal length (cm)',
predict=rf.predict)
ice_plot(forty_ice_df, c='dimgray', linewidth=0.3)
plt.ylabel('Pred. Target')
plt.xlabel('petal length (cm)')
只需通过
X\u列更改X\u列
。值