为什么我用随机森林模型每次的score都不同 差异很大

随机森林模型 回归 ，

import numpy as np
import pandas as pd
from sklearn.neighbors import KNeighborsRegressor
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score

data =pd.read_excel("data.xlsx","Sheet1")
data= data.dropna()
X=data.iloc[:,3:7]
y=data.iloc[:,10]
X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.3)
rfr = RandomForestRegressor(n_estimators=20, max_features='auto',random_state=0)
#rfr.fit(X_train,y_train)
kf = KFold(n_splits=5, shuffle=True)
score_ndarray = cross_val_score(rfr, X, y, cv=kf)
print(score_ndarray)

400多个样本；

这个是5次的结果

[ 0.220815 0.6115134 -0.72378707 0.51356938 0.36471651]