为什么使用“for”手动搜索的结果比使用 GridsearchCV 更好？

问：

我阅读了几个使用 GridsearchCV 算法查找最佳超参数的地方，但是在 knn 算法中，我将邻居数设置为 3 并使用 train_test_spilt，它显示 7057 的差异，而当我使用 GridsearchCV 查找最佳邻居时，数字是 4。当我使用 4 时，显示差异是 11473 如何增加与最佳邻居数的差异？ 法典：

import pandas as pd
from sklearn.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsRegressor
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import mean_squared_error
from math import sqrt

file_data = pd.read_csv(r"C:\Users\Ashil-Rayan\Desktop\Data\RankHome\housePricelimit2.csv", header=None)
data = file_data.drop(6, axis=1)
target = file_data[6]

z = []
X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=42)

#find best neighbor with for

for i in range(1,10):
    knn = KNeighborsRegressor(n_neighbors=i,  weights="distance",)
    knn.fit(X_train, y_train)
    predicts = knn.predict(X_test)
    result = mean_squared_error(y_test, predicts)
    result = sqrt(result)
    score = knn.score(X_test, y_test)
    z.append((i, result, score))
x = []
y = []
for i in z:
    x.append(i[0])
    y.append(i[1])
plt.scatter(x, y)
plt.xlabel("n_neighbors")
plt.ylabel("mean_squared_error")
plt.show()
print(z[1])
print(z[3])
#use GridsearchCV

parametrs = {"n_neighbors" : range(1, 50),
             "weights" : ["uniform", "distance"]}
Grid = GridSearchCV(KNeighborsRegressor(), parametrs)
Grid.fit(X_train, y_train)
print(Grid.best_params_)
# print(knn.predict([new_data]))
print(knn.score(X_test, y_test))

在此处输入图像描述

在此处输入图像描述