from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler from sklearn.feature_selection import SelectKBest, f_classif from sklearn.datasets import load_iris load_data = load_iris() # 归一化 scaler = MinMaxScaler() load_data.data = scaler.fit_transform(load_data.data) # 相关系数法 select = SelectKBest(f_classif, k=2) load_data.data = select.fit_transform(load_data.data, load_data.target) # 把数据集和标签划分成训练集和测试集 x_train, x_test, y_train, y_test = train_test_split(load_data.data, load_data.target, test_size=0.2) # 创建KNN分类器 knn = KNeighborsClassifier(n_neighbors=20) # 代入训练集 knn.fit(x_train, y_train) # 预测 y_pre = knn.predict(x_test) total = 0 right = 0 for i in range(y_pre.size): total += 1 if y_test[i] == y_pre[i]: right += 1 print("预测正确率为 : {:.2%}".format(right/total)) print(y_pre) print(y_test)
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