本文主要做的工作是:使用KNN算法实现鸢尾花数据集分类,KNN中的k值分别使用绘图和网格搜索两种方式获取,并且将分类结果可视化。
主要程序如下:
import sklearn.datasets as datasets
from matplotlib import pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_iris
plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文标签
plt.rcParams['axes.unicode_minus'] = False
程序1主要绘制准确率随k值变化曲线:
def knn_iris():
# 1)加载鸢尾花数据集
iris = datasets.load_iris()
# 打印出鸢尾花数据集
print(iris)
# 2)划分数据集
x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=2021)
# 3)标准化
transfer = StandardScaler()
x_train = transfer.fit_transform(x_train)
x_test = transfer.transform(x_test)
# 4)找knn的k值与准确率的关系,从而确定k值
k_plot(x_train, y_train)
# 5)KNN算法预估器
estimator = KNeighborsClassifier(n_neighbors=3)
estimator.fit(x_train, y_train)
# 6)模型评估
# 方法1:直接比对真实值和预测值
y_predict = estimator.predict(x_test)
print("y_predict:\n", y_predict)
print("直接比对真实值和预测值:\n", y_test == y_predict)
# 方法2:计算准确率
score = estimator.score(x_test, y_test)
print("准确率为:\n", score)
return x_test,y_test,y_predict,score
程序2主要实现网格搜索寻优:
def knn_iris_gscv():
"""
用KNN算法对鸢尾花进行分类,添加网格搜索和交叉验证
:return:
"""
# 1)获取数据
iris = load_iris()
# 2)划分数据集
x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=22)
# 3)标准化
transfer = StandardScaler()
x_train = transfer.fit_transform(x_train)
x_test = transfer.transform(x_test)
# 4)KNN算法预估器
estimator = KNeighborsClassifier()
# 加入网格搜索与交叉验证
# 参数准备
param_dict = {"n_neighbors": [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29]}
estimator = GridSearchCV(estimator, param_grid=param_dict, cv=10)
estimator.fit(x_train, y_train)
# 5)模型评估
# 方法1:比对真实值和预测值
y_predict = estimator.predict(x_test)
print("y_predict:\n", y_predict)
print("比对真实值和预测值:\n", y_test == y_predict)
# 方法2:计算准确率
score = estimator.score(x_test, y_test)
print("准确率为:\n", score)
# 最佳参数:best_params_
print("最佳参数:\n", estimator.best_params_)
# 最佳结果:best_score_
print("最佳准确率:\n", estimator.best_score_)
准确率随k值变化曲线为:
鸢尾花数据集分类结果可视化:
网格搜索结果为:
文章中可能存在多处不足,还请各位大佬批评指正。
完整代码下载请点击:https://download.csdn.net/download/weixin_44525542/86234440