这里是代码

import numpy as np
from sklearn.datasets import fetch_mldata

#PCA对数据进行降维
minst = fetch_mldata('MNIST original')
X,y = minst['data'],minst['target']
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y)
from sklearn.decomposition import PCA
pca = PCA(0.9)
pca.fit(X_train)
X_train_reduction = pca.transform(X_train)
X_test_reduction = pca.transform(X_test)

from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
def PolynomialLogisticRegression(degree = 1, C = 0.1):
    return Pipeline([
        ('poly', PolynomialFeatures(degree=degree)),
        ('std_scaler', StandardScaler()),
        ('log_reg', LogisticRegression(C=C))
    ])

# 待进行网格搜索的算法    
poly_log_reg = PolynomialLogisticRegression()
 
# 准备待搜索的参数列表
C_PARM = [0.1,0.2,0.3,0.4,0.5]
param_grid = [
    {
        'poly__degree': [i for i in range(1, 11)], 
        'log_reg__C': [i for i in C_PARM]
    }
]
 
# 实例化GridSearchCV进行网格搜索
grid_search = GridSearchCV(poly_log_reg, param_grid)
grid_search.fit(X_train_reduction ,y_train)    

跑了17分钟以后报MemoryError