#coding:utf-8 %matplotlib inline from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier import numpy as np import matplotlib.pyplot as plt iris_dataset = load_iris() # 获取数据 # print("keys of iris_dataset:\n{}".format(iris_dataset.keys())) # print(iris_dataset["DESCR"][:193]+"\n...") # print("target names:{}".format(iris_dataset["target_names"])) # print("feature names:{}".format(iris_dataset["feature_names"])) # print(iris_dataset["data"][:5]) # print(iris_dataset["data"], iris_dataset["target"]) # 对数据进行拆分,分为训练数据和测试数据 x_train, x_test, y_train, y_test = train_test_split(iris_dataset["data"], iris_dataset["target"], random_state=0) # print(x_train, x_test, y_train, y_test) knn = KNeighborsClassifier(n_neighbors=1) # 获取KNN对象 knn.fit(x_train, y_train) # 训练模型 # 评估模型 y_pre = knn.predict(x_test) score = knn.score(x_test, y_test) # 调用打分函数 print("test set predictions:\n{}".format(y_test)) print("test set score:{:.2f}".format(score)) if score > 0.9: x_new = np.array([[5, 2.9, 1, 0.3]]) print("x_new.shape:{}".format(x_new.shape)) prediction = knn.predict(x_new) # 预测 print("prediction:{}".format(prediction)) print("predicted target name:{}".format(iris_dataset["target_names"][prediction])) # 可视化展示 plt.title("KNN Classification") plt.plot(x_train, y_train, "b.") # 训练数据打点 plt.plot(x_test, y_test, "y.") # 测试数据打点 plt.plot(x_new, prediction, "ro") # 预测数据打点 plt.show() else: print("used train or test data is not available !")
knn实现鸢尾花分类
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