Iris数据集是常用的分类实验数据集,由Fisher, 1936收集整理。Iris也称鸢尾花卉数据集,是一类多重变量分析的数据集。关于数据集的具体介绍:
from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.neighbors import KNeighborsClassifier
# 1.获取数据集 iris = load_iris() # 2.数据基本处理 # x_train,x_test,y_train,y_test为训练集特征值、测试集特征值、训练集目标值、测试集目标值 x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=22)
# 3、特征工程:标准化 transfer = StandardScaler() x_train = transfer.fit_transform(x_train) x_test = transfer.transform(x_test)
# 4、机器学习(模型训练) estimator = KNeighborsClassifier(n_neighbors=9) 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("预测结果为:\n", y_predict) print("比对真实值和预测值:\n", y_predict == y_test) # 方法2:直接计算准确率 score = estimator.score(x_test, y_test) print("准确率为:\n", score)