鸢尾花识别:
from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier #声明一个鸢尾花的类对象 iris = load_iris() # 获取样本数据 iris_data = iris.data # 获取样本标记值 iris_target = iris.target print(iris_data) print(iris.target) # 获取鸢尾花的特征值，目标值 # 将数据分割成训练集和测试集 test_size=0.25表示将25%的数据用作测试集 # 分别把样本数据和它的标记数据分为训练集和测试集 x_train, x_test, y_train, y_test = train_test_split(iris_data, iris_target, test_size=0.25) # 送入算法 knn = KNeighborsClassifier(n_neighbors=5) # 创建一个KNN算法实例，n_neighbors默认为5,后续通过网格搜索获取最优参数 knn.fit(x_train, y_train) # 将测试集送入算法 y_predict = knn.predict(x_test) # 获取预测结果 # 预测结果展示 labels = ["山鸢尾","虹膜锦葵","变色鸢尾"] for i in range(len(y_predict)): print("第%d次测试:预测值:%s 真实值:%s"%((i+1),labels[y_predict[i]],labels[y_test[i]])) print("准确率：",knn.score(x_test, y_test))
1234567891011121314151617181920212223242526关于K值的选择测试：
from sklearn.datasets import load_iris from sklearn.model_selection import cross_val_score import matplotlib.pyplot as plt from sklearn.neighbors import KNeighborsClassifier font = {"family": "SimHei", "size": "20"} plt.rc("font", **font) iris = load_iris() x = iris.data y = iris.target k_range = range(1, 31) k_error = [] for k in k_range: knn = KNeighborsClassifier(n_neighbors=k) #cv参数决定数据集划分比例，这里是按照5:1划分训练集和测试集 scores = cross_val_score(knn, x, y, cv=6) print(scores) k_error.append(1 - scores.mean()) #画图，x轴为k值，y值为误差值 plt.plot(k_range, k_error) plt.xlabel("K的值") plt.ylabel("错误") plt.show()
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