k_score.append([k,knn.score(X_test,y_test)])，这行代码有什么可以替换的代码吗

如果您是在使用scikit-learn库进行KNN模型的开发，k_score.append([k,knn.score(X_test,y_test)])是将k值和KNN模型在测试集X_test和y_test上的准确率存入列表k_score中。您可以使用sklearn.model_selection中的cross_val_score函数来进行交叉验证，代码如下：

from sklearn.model_selection import cross_val_score
scores = cross_val_score(knn, X, y, cv=5)
k_score.append([k, scores.mean()])

其中，cv=5表示进行5折交叉验证，scores.mean()表示计算5个准确率的平均值作为KNN模型在当前k值下的准确率。

相关问题

from sklearn.neighbors import KNeighborsClassifier k_score = [] k_range = range(1,228) for k in range(1,228): knn = KNeighborsClassifier(n_neighbors=k) knn.fit(X_train,y_train) k_score.append([k,knn.score(X_test,y_test)]) print(k, k_score) k_score = np.array(k_score) k_score = pd.DataFrame(k_score,columns=['Value of K for KNN','Score']) ax = sns.lineplot(data=k_score,x="Value of K for KNN", y="Score",)。改写这段代码

以下是改写后的代码，主要对输出进行了优化，使用了f-string格式化字符串，并将k_score转换为pandas DataFrame后使用了seaborn绘制折线图：

import pandas as pd
import numpy as np
import seaborn as sns
from sklearn.neighbors import KNeighborsClassifier

k_score = []

for k in range(1, 228):
    knn = KNeighborsClassifier(n_neighbors=k)
    knn.fit(X_train, y_train)
    score = knn.score(X_test, y_test)
    k_score.append([k, score])
    print(f"K={k}, Score={score:.4f}")

k_score = pd.DataFrame(k_score, columns=['Value of K for KNN', 'Score'])
sns.lineplot(data=k_score, x="Value of K for KNN", y="Score")

# 封装函数来进行knn试探性运算 def knn_score(k,X,y): # 构造算法对象 knn = KNeighborsClassifier(n_neighbors = k) scores = [] train_scores = [] for i in range(100): # 拆分 X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=1) # 训练 knn.fit(X_train,y_train) # 评价模型 scores.append(knn.score(X_test,y_test)) # 经验评分 train_scores.append(knn.score(X_train,y_train)) return np.array(scores).mean(),np.array(train_scores).mean() # 调参 result_dict = {} k_list = [1,3,5,7,9,11] for k in k_list: score,train_score = knn_score(k,X,y) result_dict[k] = [score,train_score]利用这段代码调用tkinter库实现界面

可以使用`tkinter`库来创建一个窗口界面，然后在界面上添加按钮，当按钮被点击时，调用`knn_score()`函数进行试探性运算并将结果显示在界面上。

以下是一个简单的示例代码：

import tkinter as tk
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
import numpy as np

def knn_score(k,X,y):
    # 构造算法对象
    knn = KNeighborsClassifier(n_neighbors = k)
    scores = []
    train_scores = []
    for i in range(100):
        # 拆分
        X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=1)
        # 训练
        knn.fit(X_train,y_train)
        # 评价模型
        scores.append(knn.score(X_test,y_test))
        # 经验评分
        train_scores.append(knn.score(X_train,y_train))
    return np.array(scores).mean(),np.array(train_scores).mean()

# 创建窗口
root = tk.Tk()
root.title("KNN试探性运算")

# 添加标签
label1 = tk.Label(root, text="KNN试探性运算", font=("Arial", 16))
label1.pack()

# 添加按钮
button1 = tk.Button(root, text="运算", font=("Arial", 12))
button1.pack()

# 添加文本框
text1 = tk.Text(root, width=50, height=10)
text1.pack()

# 绑定按钮事件
def button_click():
    X = # 定义特征矩阵
    y = # 定义目标变量
    result_dict = {}
    k_list = [1,3,5,7,9,11]
    for k in k_list:
        score,train_score = knn_score(k,X,y)
        result_dict[k] = [score,train_score]
    # 将结果显示在文本框中
    text1.delete(1.0, tk.END)
    text1.insert(tk.END, str(result_dict))

button1.config(command=button_click)

# 运行窗口
root.mainloop()

注意：此示例代码中的`X`和`y`需要根据实际情况进行定义。