KNN（K近邻）水仙花的分类(含答案)

题目

以下采用K-NN算法来解决水仙花的分类问题，每个样本有两个特征，第一个为水仙花的花萼长度，第二个为水仙花 的花萼宽度，具体数据见表，

    1）设置k=3， 采用欧式距离，分析分类精度为多少?

    2）使用网格搜索方式找到最佳参数，并预测

    3）可视化

 

 我的数据集合就是这个

excel数据展示

 代码

import numpy as np
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
 
def model_selection(x_train, y_train):
    params = {'n_neighbors': [3,5,7,8,10], 'p': [1,2]}
    model = KNeighborsClassifier()
    gs = GridSearchCV(model, params, verbose=2, cv=5)
    gs.fit(x_train, y_train)
    print("Best Model:", gs.best_params_, "Accuracy:", gs.best_score_)
    return gs.best_estimator_
 
def read():
    filename = r"data/shuixianhua.xlsx"
    data = pd.read_excel(filename, header=None)
    x1 = data.iloc[1:, [0, 1]].values
    x2 = data.iloc[1:, [3, 4]].values
    # print(x2)
    y1 = data.iloc[1:, 2].values
    y2 = data.iloc[1:, 5].values
    x = np.vstack((x1, x2))  # 竖向合并
    y = np.hstack((y1, y2))  # 横向合并
 
    y = y.astype(int)
    return x, y
.....
需要代码，我不给了，呜呜呜