机器学习之鸢尾花案例

from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_iris

# 1、获取数据集
iris = load_iris()
# 2、数据基本处理 -- 划分数据集
x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=22)
# 3、特征工程：标准化
# 实例化一个转换器类
transfer = StandardScaler()
# 调用fit_transform
x_train = transfer.fit_transform(x_train)
x_test = transfer.transform(x_test)
# 4、KNN预估器流程
#  4.1 实例化预估器类
estimator = KNeighborsClassifier()

# 4.2 模型选择与调优——网格搜索和交叉验证
# 准备要调的超参数
param_dict = {"n_neighbors": [1, 3, 5],'weights':["uniform","distance"]}
estimator = GridSearchCV(estimator, param_grid=param_dict, cv=3)
# 4.3 fit数据进行训练
estimator.fit(x_train, y_train)
# 5、评估模型效果
# 方法a：比对预测结果和真实值
y_predict = estimator.predict(x_test)
print("比对预测结果和真实值：\n", y_predict == y_test)
# 方法b：直接计算准确率
score = estimator.score(x_test, y_test)
print("直接计算准确率：\n", score)
print("在交叉验证中验证的最好结果：\n", estimator.best_score_)
print("最好的参数模型：\n", estimator.best_params_)

显示结果：

在交叉验证中验证的最好结果：
0.9732100521574205
最好的参数模型：
{'n_neighbors': 5, 'weights': 'uniform'}