knn_classifier = KNeighborsClassifier(n_neighbors=5 ) # 喂入数据集,以及数据类型 knn_classifier.fit(x_train,y_train) # 如果关心预测结果可以跳过下面所有score返回得分 knn_classifier.score(x_test,y_test) y_predict = knn_classifier.predict(x_test) # 评价预测结果 将y_predict和真是的predict进行...
from sklearn.neighbors import KNeighborsClassifier kNN_classifier = KNeighborsClassifier(n_neighbors=6) kNN_classifier.fit(X_train, y_train) #预测 kNN_classifier.predict(x) #转化为矩阵 X_predict = x.reshape(1, -1) kNN_classifier.predict(X_predict) y_predict = kNN_classifier.predict(X_predi...
引入KNN分类器: KNN Classifier(K近邻分类): 训练阶段:只需要记录每一个样本的类别即可 测试阶段:计算新图像x与每一个训练样本x(i)的距离d(x,x(i)) 找出与x距离最近的k个训练样本 用这k个训练样本中最多数类别作为x的类别 图中在训练阶段记住了哪些是红色的,哪些是黑色以及蓝色,接着在预测时输入的绿色点...
#第五步 KNN分类from sklearn.neighbors import KNeighborsClassifierknn = KNeighborsClassifier(n_neighbors=2,p=2,metric="minkowski")knn.fit(X_train_std,y_train)res2 = knn.predict(X_test_std)print(res2)print(metrics.classification_report(y_test, res2, digits=4)) #四位小数plot_decision_regio...
class KNNClassifier: def __init__(self, k): """初始化kNN分类器""" assert k >= 1, "k must be valid" self.k = k self._X_train = None self._y_train = None def fit(self, X_train, y_train): """根据训练数据集X_train和y_train训练kNN分类器""" ...
knn = KNeighborsClassifier(n_neighbors=3)# 训练模型 knn.fit(X_train, y_train)# 预测测试集 y_pred = knn.predict(X_test)# 评估模型 accuracy = accuracy_score(y_test, y_pred)print(f"Model accuracy: {accuracy:.2f}")在这个例子中，我们首先导入所需的库和模块，然后加载鸢尾花数据集，将其...
KNN Classifier 原创转载请注明出处:https://www.cnblogs.com/agilestyle/p/12668372.html 准备数据 importmatplotlib.pyplot as pltimportnumpy as npfromsklearn.datasetsimportmake_blobsfromsklearn.neighborsimportKNeighborsClassifier centers= [[1, 1], [-1, -1], [1, -1]]...
Mdlis a trainedClassificationKNNclassifier, and some of its properties appear in the Command Window. To access the properties ofMdl, use dot notation. Mdl.ClassNames ans =3x1 cell{'setosa' } {'versicolor'} {'virginica' } Mdl.Prior
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"] ...
classifier.fit(X_train, y_train) 在拟合之后,我们可以预测测试数据的类别: 评估KNN 进行分类 要评估 KNN 分类器,我们可以使用score方法,但它执行不同的度量标准,因为我们评分的是分类器而不是回归器。 让我们评分我们的分类器: python acc = classifier.score(X_test, y_test) ...