KNN=KNearestNeighbor() KNN.train(X_train, y_train) y_pred = KNN.predict(X_test, k=6) accuracy = np.mean(y_pred == y_test) print('测试集预测准确率：%f' % accuracy) plt.scatter(X_setosa_train[:, 0], X_setosa_train[:, 2], color='red', marker='o', label='setosa_train') plt.scatter(X_versicolor_train[:, 0], X_versicolor_train[:, 2], color='blue', marker='^', label='versicolor_train') plt.scatter(X_virginica_train[:, 0], X_virginica_train[:, 2], color='green', marker='s', label='virginica_train') plt.scatter(X_setosa_test[:, 0], X_setosa_test[:, 2], color='y', marker='o', label='setosa_test') plt.scatter(X_versicolor_test[:, 0], X_versicolor_test[:, 2], color='y', marker='^', label='versicolor_test') plt.scatter(X_virginica_test[:, 0], X_virginica_test[:, 2], color='y', marker='s', label='virginica_test') plt.xlabel('sepal length') plt.ylabel('petal length') plt.legend(loc = 4) plt.show()注释每一行代码

KNN算法的代码

KNN近邻算法的应用，第一个应用是简单的一个测试，第二个应用是改进约会网站的配对效果，第三个应用是手写识别系统

KNN最近邻算法java实现

x_train,x_test,y_train,y_test = train_test_split(data.iloc[:,:-1],data.iloc[:,-1], test_size=0.2, random_state=66) x_train = x_train.astype('float') y_train = y_train.astype('int') x_test = x_test.astype('float') y_test = y_test.astype('int') knn = KNeighborsClassifier(n_neighbors=10) knn.fit(x_train, y_train) y_pred = knn.predict(x_test) knn_cvscore = cross_val_score(knn,x_train,y_train,cv=5,scoring='accuracy') knn_cvmean = np.mean(knn_cvscore) print('Test score(accuracy)',knn.score(x_test,y_test)) knn_f1 = f1_score(y_test,y_pred,average='macro') print('F1 score:',knn_f1) knn_acc = accuracy_score(y_test,y_pred) print('Accuracy:',knn_acc)

这段代码是使用K近邻算法（K-Nearest Neighbors, KNN）对数据进行分类，并输出了测试集上的准确率（Test score），F1分数（F1 score），以及准确度（Accuracy）。同时，还使用了交叉验证（Cross Validation）来评估...

from sklearn.neighbors import KNeighborsRegressor import pandas as pd KNN = KNeighborsRegressor(n_neighbors=7,weights='distance',p=1) KNN.fit(X_train, Y_train) Y_train_pred=KNN.predict(X_train) Y_test_pred = KNN.predict(X_test) print ("K近邻模型评估--训练集：") print ('r²:',KNN.score(X_train,Y_train)) print ('均方差',mean_squared_error(Y_train,Y_train_pred)) print ("K近邻模型评估--验证集：") print ('r²:',KNN.score(X_test,Y_test)) print ('均方差',mean_squared_error(Y_test,Y_test_pred))

其中，X_train和Y_train是训练集的自变量和因变量，X_test和Y_test是验证集的自变量和因变量。在训练完成后，使用模型对训练集和验证集进行预测，并输出模型的评估指标，包括r²和均方差。其中，r²表示拟合程度的...

import sklearn.model_selection as ms datas=pd.read_csv(r'C:/Users/20397/Desktop/人工智能实训材料/Day3 分类/wisc_bc_data.csv',sep=',') x=datas.iloc[:,2:32] y=datas.iloc[:,1:2] x_train,x_test,y_train,y_test=ms.train_test_split(x,y,test_size=0.2,random_state=42) y_train=y_train.values.ravel() y_test=y_test.values.ravel() #构建和训练模型 import numpy as np import matplotlib.pyplot as plt from sklearn.neighbors import KNeighborsClassifier import pandas as pd # 构建 knn 模型 knn= KNeighborsClassifier(n_neighbors=43,weights='distance',p=2) #训练KNN模型 knn.fit(x_train,y_train) knn.score(x_train,y_train) # 评估模型效果 from sklearn.metrics import classification_report y_pred=knn.predict(x_test) print(classification_report(y_test,y_pred))

首先，使用 pandas 读取 csv 文件中的数据，并对数据进行切分，将前 30 列作为特征 x，第 2 列作为标签 y。然后使用 sklearn 中的 train_test_split 函数将数据切分为训练集和测试集，其中测试集占比为 20%。接着，...

import sklearn.model_selection as ms datas=pd.read_csv(r'C:/Users/20397/Desktop/人工智能实训材料/Day3 分类/wisc_bc_data.csv',sep=',') x=datas.iloc[:,2:32] y=datas.iloc[:,1:2] x_train,x_test,y_train,y_test=ms.train_test_split(x,y,test_size=0.2,random_state=42) y_train=y_train.values.ravel() y_test=y_test.values.ravel() #构建和训练模型 import numpy as np import matplotlib.pyplot as plt from sklearn.neighbors import KNeighborsClassifier import pandas as pd # 构建 knn 模型 knn= KNeighborsClassifier(n_neighbors=43,weights='distance',p=2) #训练KNN模型 knn.fit(x_train,y_train) knn.score(x_train,y_train) # 评估模型效果 from sklearn.metrics import classification_report y_pred=knn.predict(x_test) print(classification_report(y_test,y_pred))讲一下

2. 分割数据集：使用sklearn库中的train_test_split函数将数据集分为训练集和测试集。 3. 构建模型：使用sklearn库中的KNeighborsClassifier函数构建KNN分类器模型，并指定一些参数。 4. 训练模型：使用fit函数对...

2.用KNN建立分类模型，分析模型的准确率。（30分） from sklearn import neighbors knn = neighbors.KNeighborsClassifier() knn.fit(X_train, y_train) y_pred_knn = knn.predict(X_test) 3.采用DecisionTreeClassifier决策树模型建模，比较各因素的重要性(feature_importances_)，并分析模型的准确率。（40分） from sklearn import tree dtc = tree.DecisionTreeClassifier() dtc.fit(X_train, y_train) y_pred_dtc = dtc.predict(X_test)

y_pred_knn = knn.predict(X_test) acc_knn = knn.score(X_test, y_test) print("KNN Accuracy:", acc_knn) 在拟合数据之后，我们可以使用predict()方法来进行预测，使用score()方法来计算准确率。需要注意的是...

from sklearn.neighbors import KNeighborsClassifier knn_model = KNeighborsClassifier() knn_model.fit(X_train_std,y_train) print(knn_model.score(X_train_std,y_train)) print(knn_model.score(X_test_std,y_test)) from sklearn.metrics import classification_report, confusion_matrix y_pred =knn_model.predict(X_test) print(classification_report(y_test,y_pred))分行解释代码

- y_pred = knn_model.predict(X_test)：使用训练好的 k 近邻分类器模型对测试数据 X_test 做出预测。 - print(classification_report(y_test, y_pred))：打印分类器模型在测试数据上的分类报告，包括精确率、...

from sklearn.neighbors import KNeighborsClassifier knn_model = KNeighborsClassifier() knn_model.fit(X_train_std,y_train) print(knn_model.score(X_train_std,y_train)) print(knn_model.score(X_test_std,y_test)) from sklearn.metrics import classification_report, confusion_matrix y_pred =knn_model.predict(X_test) print(classification_report(y_test,y_pred)) cm = confusion_matrix(y_test, y_pred) plt.figure(figsize = (8,8)) sns.heatmap(cm,cmap= "Blues", linecolor = 'black' , linewidth = 1 , annot = True, fmt='' , xticklabels = ['A','B','C','D'] , yticklabels = ['A','B','C','D']) plt.xlabel("Predicted") plt.ylabel("Actual") plt.show() from sklearn.neighbors import KNeighborsClassifier knn_model = KNeighborsClassifier() prams ={ 'n_neighbors':[13,15,18,22]} gd_knn= GridSearchCV(knn_model , param_grid=prams, n_jobs=-1 ,cv=10) gd_knn.fit(X_train_std , y_train) print(gd_knn.best_estimator_) tuned_knn = gd_knn.best_estimator_ print(f'Train : {tuned_knn.score(X_train_std, y_train)}') model_eval(tuned_knn,X_test_std,y_test)

这段代码使用了scikit-learn库中的KNeighborsClassifier类来创建一个K近邻分类器，并使用fit方法对标准化后的训练数据进行拟合。接着，使用score方法计算了模型在训练集和测试集上的准确率，并将其打印输出。之后，...

smote = SMOTE(random_state=42) X_res, y_res = smote.fit_resample(X_knn, y_knn) X_train, X_test, y_train, y_test = train_test_split(X_res, y_res, test_size=0.4) clf = RandomForestClassifier() clf.fit(X_train, y_train) y_pred = clf.predict(X_test) result_sm = classification_report(y_test, clf.predict(X_test)) #print(result) testing_acc_ = clf.score(X_test, y_test) * 100 print('随机森林分类测试准确率: {:.2f}%'.format(testing_acc_))

然后使用fit_resample方法对特征矩阵X_knn和目标变量y_knn进行过采样处理，得到过采样后的特征矩阵X_res和目标变量矩阵y_res。接着将数据集划分为训练集和测试集，其中测试集占总样本数量的40%。然后使用...

import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.metrics import classification_report from sklearn.metrics import confusion_matrix fruits = pd.read_table('fruit_data_with_colors.txt') fruits.head() fruits.shape print(fruits['fruit_name'].unique()) fruits.drop('fruit_label', axis=1).hist(bins=30, figsize=(9,9)) plt.suptitle("Histogram for each numeric input variable") plt.show() feature_names = ['mass', 'width', 'height', 'color_score'] x = fruits[feature_names] y = fruits['fruit_label'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0) scaler = MinMaxScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) logreg = LogisticRegression() logreg.fit(X_train, y_train) clf = DecisionTreeClassifier().fit(X_train, y_train) knn = KNeighborsClassifier() knn.fit(X_train, y_train) svm = SVC() svm.fit(X_train, y_train) pred = knn.predict(X_test)

你给出的代码是一个简单的机器学习分类问题的示例代码，使用的是...其中，MinMaxScaler用于对数据进行归一化处理，train_test_split用于将数据集分为训练集和测试集。最后使用K近邻分类器进行预测，并输出预测结果。

请教学式按句详细讲解以下代码：###--------------------KNN算法与决策树算法-------------------- from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split # 将文本数据转化为数值特征 vectorizer = TfidfVectorizer() X = vectorizer.fit_transform(data_str_list) # 划分数据集为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 特征缩放 scaler = StandardScaler() X_train = scaler.fit_transform(X_train.toarray()) X_test = scaler.transform(X_test.toarray()) from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import GridSearchCV from sklearn.metrics import accuracy_score # 使用网格搜索进行超参数调优 param_grid = { "n_neighbors": [3, 5, 7, 9], "weights": ["uniform", "distance"], "algorithm": ["auto", "ball_tree", "kd_tree", "brute"] } knn = KNeighborsClassifier() grid_search = GridSearchCV(knn, param_grid, cv=5) grid_search.fit(X_train, y_train) print("KNN最优参数：", grid_search.best_params_) param_grid = { "criterion": ["gini", "entropy"], "max_depth": [3, 5, 7, 9] } dt = DecisionTreeClassifier() grid_search = GridSearchCV(dt, param_grid, cv=5) grid_search.fit(X_train, y_train) print("决策树最优参数：", grid_search.best_params_) # 训练分类器并进行预测 knn = KNeighborsClassifier(n_neighbors=5, weights="uniform", algorithm="auto") knn.fit(X_train, y_train) knn_pred = knn.predict(X_test) dt = DecisionTreeClassifier(criterion="gini", max_depth=9) dt.fit(X_train, y_train) dt_pred = dt.predict(X_test) # 混合使用KNN和决策树进行文本分类 ensemble_pred = [] for i in range(len(knn_pred)): if knn_pred[i] == dt_pred[i]: ensemble_pred.append(knn_pred[i]) else: ensemble_pred.append(knn_pred[i]) # 输出分类结果和准确率 print("KNN准确率：", accuracy_score(y_test, knn_pred)) print("决策树准确率：", accuracy_score(y_test, dt_pred)) print("混合使用准确率：", accuracy_score(y_test, ensemble_pred))

knn_pred = knn.predict(X_test) dt = DecisionTreeClassifier(criterion="gini", max_depth=9) dt.fit(X_train, y_train) dt_pred = dt.predict(X_test) 使用最优的超参数组合训练分类器，并对测试集进行预测...

from sklearn import datasets from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt import numpy as np # 加载数据集 iris = datasets.load_iris() X = iris.data y = iris.target # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3) # 创建KNN分类器，K=3 knn = KNeighborsClassifier(n_neighbors=3) # 训练模型 knn.fit(X_train, y_train) # 在测试集上进行预测 y_pred = knn.predict(X_test) # 计算准确率 accuracy = knn.score(X_test, y_test) print("准确率为：", accuracy) # 绘制分类分布结果的二维图 colors = ['red', 'green', 'blue'] markers = ['o', 's', 'x'] for i, color, marker in zip(np.unique(y_pred), colors, markers): plt.scatter(X_test[y_pred == i, 0], X_test[y_pred == i, 1], color=color, marker=marker, label=iris.target_names[i]) plt.legend() plt.xlabel(iris.feature_names[0]) plt.ylabel(iris.feature_names[1]) plt.show()

这段代码使用了scikit-learn库中的K近邻算法实现对鸢尾花数据集的分类。首先，通过导入数据集并将其分为训练集和测试集。然后创建了一个KNeighborsClassifier对象，其中传递参数n_neighbors=3表示使用3个邻居来进行...

class KNearestNeighbor(object): def init(self): pass def train(self, X, y): self.X_train = X self.y_train = y def predict(self, X, k=1): num_test = X.shape[0] num_train = self.X_train.shape[0] dists = np.zeros((num_test, num_train)) d1 = -2 * np.dot(X, self.X_train.T) d2 = np.sum(np.square(X), axis=1, keepdims=True) d3 = np.sum(np.square(self.X_train), axis=1) dist = np.sqrt(d1 + d2 + d3) y_pred = np.zeros(num_test) for i in range(num_test): dist_k_min = np.argsort(dist[i])[:k] y_kclose = self.y_train[dist_k_min] y_pred[i] = np.argmax(np.bincount(y_kclose.tolist())) return y_pred注释每一行代码

y_kclose = self.y_train[dist_k_min] # 在k个标签中找到出现次数最多的标签，作为预测结果 y_pred[i] = np.argmax(np.bincount(y_kclose.tolist())) return y_pred KNN算法是一种比较简单的分类算法，...

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy_score # 导入数据集 data = pd.read_csv("1.csv") # 分割特征和标签 X = data.iloc[:, :-1] y = data.iloc[:, -1] # 将数据集分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) # 创建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) # 计算准确率 accuracy = accuracy_score(y_test, y_pred) print("Accuracy:", accuracy) # 假设有一个新的样本 [5.1, 3.5, 1.4] new_sample = [[1, 27, 19]] # 使用训练好的KNN分类器进行预测 pred = knn.predict(new_sample) # 输出预测结果 print("预测结果:", pred)写一个和这个类似的代码

y_pred = knn.predict(X_test) # 计算准确率 accuracy = knn.score(X_test, y_test) print("Accuracy:", accuracy) # 假设有一个新的样本 [5.1, 3.5, 1.4, 0.2] new_sample = [[5.1, 3.5, 1.4, 0.2]] # 使用训练...

以下是使用KNN算法的过程，请详细解释以下代码，说明每个步骤如何实现，尤其需要说明调参过程的每句代码的用途以及做法来源：param_grid = { "n_neighbors": [3, 5, 7, 9], "weights": ["uniform", "distance"], "algorithm": ["auto", "ball_tree", "kd_tree", "brute"] } knn = KNeighborsClassifier() grid_search = GridSearchCV(knn, param_grid, cv=5) grid_search.fit(X_train, y_train) print("KNN最优参数：", grid_search.best_params_) knn = KNeighborsClassifier(n_neighbors=5, weights="uniform", algorithm="auto") knn.fit(X_train, y_train) knn_pred = knn.predict(X_test)

8. knn_pred = knn.predict(X_test) 这里使用训练好的KNN分类器对测试数据进行预测，得到预测结果knn_pred。至此，这段代码的作用就是使用KNN算法对训练数据进行训练，通过交叉验证和网格搜索来寻找最优的参数...

import numpy as np import matplotlib.pyplot as plt import pandas as pd path="https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data" headernames=['sepal-length','sepal-width','petal-length','petal-width','Class'] dataset=pd.read_csv(path,names=headernames) dataset.head() X=dataset.iloc[:,:-1].values y=dataset.iloc[:,4].values from sklearn.model_selection import train_test_split X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.40) from sklearn.neighbors import KNeighborsClassifier classifier=KNeighborsClassifier(n_neighbors=6) classifier.fit(X_train,y_train) y_pred=classifier.predict(X_test) from sklearn.metrics import classification_report,confusion_matrix,accuracy_score result = confusion_matrix(y_test,y_pred) print("Confusion Matrix:") print(result) result1=classification_report(y_test,y_pred) print(result1) result2=accuracy_score(y_test,y_pred) print("Accuracy:",result2)

这是一个基于鸢尾花数据集的KNN分类器的Python代码，其目的是将数据集分为三个不同的类别。代码首先导入了必要的库，然后读取鸢尾花数据集并将其存储在一个Pandas DataFrame中。接下来，将数据集分成训练集和测试集...

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