sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test)

X=dataset.iloc[:,2:-1] y=dataset.iloc[:,1] print(dataset.shape) from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=0) from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train =sc.fit_transform(X_train) X_test= sc.transform(X_test) from sklearn.ensemble import RandomForestRegressor rf=RandomForestRegressor(n_estimators=200,random_state=0) #rf.fit(X_train, y_train) #拟合训练集 rf.fit(X_train, y_train) #对训练集和测试集进行预测 y_train_pred=rf.predict(X_train) y_test_pred=rf.predict(X_test) print(y_test_pred)

这段代码是一个随机森林回归...接下来，使用StandardScaler对X_train和X_test进行标准化处理。最后，使用RandomForestRegressor对训练集进行拟合，然后分别对训练集和测试集进行预测，并将测试集的预测结果打印出来。

# Importing the dataset dataset = pd.read_csv('Iris.csv') X = dataset.iloc[:, :-1].values y = dataset.iloc[:, -1].values # Splitting the dataset into the Training set and Test set from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0) # Feature Scaling from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test) # Training the Decision Tree Classification model on the Training set from sklearn.tree import DecisionTreeClassifier classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0) classifier.fit(X_train, y_train) # Making the Confusion Matrix from sklearn.metrics import confusion_matrix, accuracy_score y_pred = classifier.predict(X_test) cm = confusion_matrix(y_test, y_pred) print(cm) print(accuracy_score(y_test, y_pred))解释每行代码

X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test) 这一部分代码使用StandardScaler函数对特征进行标准化处理，将特征缩放到均值为0，方差为1的标准正态分布中。 python # 使用决策树算法...

import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from keras.models import Sequential from keras.layers import Dense from pyswarm import pso import matplotlib.pyplot as plt from sklearn.preprocessing import StandardScaler from sklearn.metrics import mean_absolute_error from sklearn.metrics import mean_squared_error from sklearn.metrics import r2_score file = "zhong.xlsx" data = pd.read_excel(file) #reading file X=np.array(data.loc[:,'种植密度':'有效积温']) y=np.array(data.loc[:,'产量']) y.shape=(185,1) # 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=0.25, random_state=10) SC=StandardScaler() X_train=SC.fit_transform(X_train) X_test=SC.fit_transform(X_test) y_train=SC.fit_transform(y_train) y_test=SC.fit_transform(y_test) print("X_train.shape:", X_train.shape) print("X_test.shape:", X_test.shape) print("y_train.shape:", y_train.shape) print("y_test.shape:", y_test.shape) # 定义BP神经网络模型 def nn_model(X): model = Sequential() model.add(Dense(8, input_dim=X_train.shape[1], activation='relu')) model.add(Dense(12, activation='relu')) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') return model # 定义适应度函数 def fitness_func(X): model = nn_model(X) model.fit(X_train, y_train, epochs=60, verbose=2) score = model.evaluate(X_test, y_test, verbose=2) print(score) # 定义变量的下限和上限 lb = [5, 5] ub = [30, 30] # 利用PySwarm库实现改进的粒子群算法来优化BP神经网络预测模型 result = pso(fitness_func, lb, ub) # 输出最优解和函数值 print('最优解:', result[0]) print('最小函数值:', result[1]) mpl.rcParams["font.family"] = "SimHei" mpl.rcParams["axes.unicode_minus"] = False # 绘制预测值和真实值对比图 model = nn_model(X) model.fit(X_train, y_train, epochs=60, verbose=2) y_pred = model.predict(X_test) y_true = SC.inverse_transform(y_test) y_pred=SC.inverse_transform(y_pred) plt.figure() plt.plot(y_true,"bo-",label = '真实值') plt.plot(y_pred,"ro-", label = '预测值') plt.title('神经网络预测展示') plt.xlabel('序号') plt.ylabel('产量') plt.legend(loc='upper right') plt.show() print("R2 = ",r2_score(y_test, y_pred)) # R2 # 绘制损失函数曲线图 model = nn_model(X) history = model.fit(X_train, y_train, epochs=60, validation_data=(X_test, y_test), verbose=2) plt.plot(history.history['loss'], label='train') plt.plot(history.history['val_loss'], label='test') plt.legend() plt.show() mae = mean_absolute_error(y_test, y_pred) print('MAE: %.3f' % mae) mse = mean_squared_error(y_test, y_pred) print('mse: %.3f' % mse)

X_train=SC.fit_transform(X_train) X_test=SC.transform(X_test) y_train=SC.fit_transform(y_train) y_test=SC.transform(y_test) 另外，你在训练模型时，对于y_train和y_test也进行了标准化，这是不正确...

以下是完整代码，请你分析一下for i in labels: df2[' Label'] = df[' Label'].apply(lambda x: labeller(x, i)) train, test=train_test_split(df2,test_size=0.2, random_state=101) scaler = StandardScaler() cols = train.select_dtypes(include=['float64','int64']).columns sc_train = scaler.fit_transform(train.select_dtypes(include=['float64','int64'])) sc_test = scaler.fit_transform(test.select_dtypes(include=['float64','int64'])) sc_traindf = pd.DataFrame(sc_train, columns = cols) sc_testdf = pd.DataFrame(sc_test, columns = cols) train_X=sc_traindf train_y=trainDep[:,0] test_X=sc_testdf test_y=testDep[:,0] X_train,X_test,Y_train,Y_test = train_test_split(train_X,train_y,train_size=0.80, random_state=101) KNN_Classifier = KNeighborsClassifier(n_jobs=-1) KNN_Classifier.fit(X_train, Y_train) accuracy = metrics.accuracy_score(Y_train, v.predict(X_train)) avg_accuracy += accuracy avg_accuracy /= len(labels) print("Avg. accuracy", avg_accuracy)

4. 对训练集进行标准化处理，使用StandardScaler()函数； 5. 将标准化后的训练集和测试集转化为DataFrame类型； 6. 将训练集划分为训练数据和验证数据，比例为0.8:0.2； 7. 使用KNN算法进行分类，将训练数据拟合到...

import pandas as pd data = pd.read_csv('heart.csv') # 目标值和特征值 x = data.drop(['HeartDisease'], axis=1) y = data['HeartDisease'] # 导入库 from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler # 划分数据集 x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=20) # 特征预处理 sc = StandardScaler() # 标准化 x_train = sc.fit_transform(x_train) x_test = sc.transform(x_test) # 导入相关库 from sklearn.metrics import accuracy_score from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report classifier = DecisionTreeClassifier(criterion="gini") # CART算法 classifier.fit(x_train, y_train.ravel()) y_pred1 = classifier.predict(x_test) # 计算准确率 score1 = classifier.score(x_test, y_test) print("准确率为：\n", score1) # 查看精确率、召回率、F1-score report1 = classification_report(y_test, y_pred1, labels=[0, 1], target_names=['Not sick', 'sick']) print(report1)请指出这段机器学习模型中的问题

1. 数据预处理不完整：虽然特征值x进行了标准化处理，但目标值y并没有进行任何处理。在训练模型之前，通常需要对目标值进行编码或者标准化处理，以确保模型的准确性和稳定性。 2. 模型评估不全面：虽然计算了模型的...

下面的这段python代码，哪里有错误，修改一下：import numpy as np import matplotlib.pyplot as plt import pandas as pd import torch import torch.nn as nn from torch.autograd import Variable from sklearn.preprocessing import MinMaxScaler training_set = pd.read_csv('CX2-36_1971.csv') training_set = training_set.iloc[:, 1:2].values def sliding_windows(data, seq_length): x = [] y = [] for i in range(len(data) - seq_length): _x = data[i:(i + seq_length)] _y = data[i + seq_length] x.append(_x) y.append(_y) return np.array(x), np.array(y) sc = MinMaxScaler() training_data = sc.fit_transform(training_set) seq_length = 1 x, y = sliding_windows(training_data, seq_length) train_size = int(len(y) * 0.8) test_size = len(y) - train_size dataX = Variable(torch.Tensor(np.array(x))) dataY = Variable(torch.Tensor(np.array(y))) trainX = Variable(torch.Tensor(np.array(x[1:train_size]))) trainY = Variable(torch.Tensor(np.array(y[1:train_size]))) testX = Variable(torch.Tensor(np.array(x[train_size:len(x)]))) testY = Variable(torch.Tensor(np.array(y[train_size:len(y)]))) class LSTM(nn.Module): def init(self, num_classes, input_size, hidden_size, num_layers): super(LSTM, self).init() self.num_classes = num_classes self.num_layers = num_layers self.input_size = input_size self.hidden_size = hidden_size self.seq_length = seq_length self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True) self.fc = nn.Linear(hidden_size, num_classes) def forward(self, x): h_0 = Variable(torch.zeros( self.num_layers, x.size(0), self.hidden_size)) c_0 = Variable(torch.zeros( self.num_layers, x.size(0), self.hidden_size)) # Propagate input through LSTM ula, (h_out, _) = self.lstm(x, (h_0, c_0)) h_out = h_out.view(-1, self.hidden_size) out = self.fc(h_out) return out num_epochs = 2000 learning_rate = 0.001 input_size = 1 hidden_size = 2 num_layers = 1 num_classes = 1 lstm = LSTM(num_classes, input_size, hidden_size, num_layers) criterion = torch.nn.MSELoss() # mean-squared error for regression optimizer = torch.optim.Adam(lstm.parameters(), lr=learning_rate) # optimizer = torch.optim.SGD(lstm.parameters(), lr=learning_rate) runn = 10 Y_predict = np.zeros((runn, len(dataY))) # Train the model for i in range(runn): print('Run: ' + str(i + 1)) for epoch in range(num_epochs): outputs = lstm(trainX) optimizer.zero_grad() # obtain the loss function loss = criterion(outputs, trainY) loss.backward() optimizer.step() if epoch % 100 == 0: print("Epoch: %d, loss: %1.5f" % (epoch, loss.item())) lstm.eval() train_predict = lstm(dataX) data_predict = train_predict.data.numpy() dataY_plot = dataY.data.numpy() data_predict = sc.inverse_transform(data_predict) dataY_plot = sc.inverse_transform(dataY_plot) Y_predict[i,:] = np.transpose(np.array(data_predict)) Y_Predict = np.mean(np.array(Y_predict)) Y_Predict_T = np.transpose(np.array(Y_Predict))

training_data = sc.fit_transform(training_set) # 定义窗口长度 seq_length = 1 # 对数据进行窗口划分 x, y = sliding_windows(training_data, seq_length) # 划分训练集和测试集 train_size = int(len(y) * ...

X_test = sc.transform([[0.03237, 0.0, 2.18, 0, 0.458, 6.998, 45.8, 6.0622, 3, 222, 18.7, 394.63, 2.94]])报错

X_train = sc.fit_transform(X_train) # 定义测试数据 X_test = np.array([[0.03237, 0.0, 2.18]]) # 对测试数据进行标准化 X_test = sc.transform(X_test) print(X_test) 在这个例子中，我们首先创建了一个...

请根据以下代码，补全并完成任务代码：作业：考虑Breast_Cancer-乳腺癌数据集总类别数为2 特征数为30 样本数为569（正样本212条，负样本357条）特征均为数值连续型、无缺失值 (1)使用GridSearchCV搜索单个DecisionTreeClassifier中max_samples,max_features,max_depth的最优值。 (2)使用GridSearchCV搜索BaggingClassifier中n_estimators的最佳值。 (3)考虑BaggingClassifier中的弱分类器使用SVC（可以考虑是否使用核函数），类似步骤(1),(2)，自己调参（比如高斯核函数的gamma参数,C参数），寻找最优分类结果。from sklearn.datasets import load_breast_cancer from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap ds_breast_cancer = load_breast_cancer() X=ds_breast_cancer.data y=ds_breast_cancer.target # draw sactter f1 = plt.figure() cm_bright = ListedColormap(['r', 'b', 'g']) ax = plt.subplot(1, 1, 1) ax.set_title('breast_cancer') ax.scatter(X[:, 0], X[:, 1], c=y, cmap=cm_bright, edgecolors='k') plt.show() #（1） from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import GridSearchCV from sklearn.preprocessing import StandardScaler # 数据预处理 sc = StandardScaler() X_std = sc.fit_transform(X) # 定义模型，添加参数 min_samples_leaf tree = DecisionTreeClassifier(min_samples_leaf=1) # 定义参数空间 param_grid = {'min_samples_leaf': [1, 2, 3, 4, 5], 'max_features': [0.4, 0.6, 0.8, 1.0], 'max_depth': [3, 5, 7, 9, None]} # 定义网格搜索对象 clf = GridSearchCV(tree, param_grid=param_grid, cv=5) # 训练模型 clf.fit(X_std, y) # 输出最优参数 print("Best parameters:", clf.best_params_) #（2） from sklearn.ensemble import BaggingClassifier # 定义模型 tree = DecisionTreeClassifier() bagging = BaggingClassifier(tree) # 定义参数空间 param_grid = {'n_estimators': [10, 50, 100, 200, 500]} # 定义网格搜索对象 clf = GridSearchCV(bagging, param_grid=param_grid, cv=5) # 训练模型 clf.fit(X_std, y) # 输出最优参数 print("Best parameters:", clf.best_params_)

X_std = sc.fit_transform(X) # 定义弱分类器 svc = SVC(kernel='rbf', probability=True) tree = DecisionTreeClassifier() # 定义模型 bagging = BaggingClassifier(base_estimator=svc) # 定义参数空间 param_...

请在jupyter中，完成并补全以下任务代码：作业：考虑Breast_Cancer-乳腺癌数据集总类别数为2 特征数为30 样本数为569（正样本212条，负样本357条）特征均为数值连续型、无缺失值 (1)使用GridSearchCV搜索单个DecisionTreeClassifier中max_samples,max_features,max_depth的最优值。 (2)使用GridSearchCV搜索BaggingClassifier中n_estimators的最佳值。 (3)考虑BaggingClassifier中的弱分类器使用SVC（可以考虑是否使用核函数），类似步骤(1),(2)，自己调参（比如高斯核函数的gamma参数,C参数），寻找最优分类结果。 from sklearn.datasets import load_breast_cancer from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap ds_breast_cancer = load_breast_cancer() X=ds_breast_cancer.data y=ds_breast_cancer.target # draw sactter f1 = plt.figure() cm_bright = ListedColormap(['r', 'b', 'g']) ax = plt.subplot(1, 1, 1) ax.set_title('breast_cancer') ax.scatter(X[:, 0], X[:, 1], c=y, cmap=cm_bright, edgecolors='k') plt.show()

X_std = sc.fit_transform(X) # 定义模型 tree = DecisionTreeClassifier() # 定义参数空间 param_grid = {'max_samples': [0.4, 0.6, 0.8, 1.0], 'max_features': [0.4, 0.6, 0.8, 1.0], 'max_depth': [3, 5, 7...

请基于“类别1-患病.xls”和“类别2-正常.xls”2个表格的数据确定对该疾病进行预测的最佳模型，利用该模型对test_data目录下“test.xls”表格中的患者进行预测，并在最后一列“预测类别”中标记预测结果，患病标记为“患病”，正常标记为“正常”

x = sc.fit_transform(x) # 划分训练集和测试集 from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2) # 建立模型 model = tf.keras...

学生成绩预测模型_逻辑回归实战练习——根据学生成绩预测是否被录取，使用sigmoid函数

X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test) 4. 训练逻辑回归模型 python from sklearn.linear_model import LogisticRegression classifier = LogisticRegression(random_state=0...

请编写代码，利用lstm模型进行时间序列预测，使其仅对序列进行单步预测，随后通过拟合窗口与预测窗口随时间向右滑动，得到关于该序列的完整预测序列，其中加载数据时读取时间序列为r"D:\xiangmu\utilits\name_of_your_excel_file3.xls"文件路径中第5列前3000行的值，将这3000个数据分为训练集与测试集，训练模型后，利用模型预测第3000-3500行的数据，并将预测结果输出到excel文档中

model.fit(train_X, train_Y, epochs=100, batch_size=32) # 在测试集上进行预测 predicted = model.predict(test_X) predicted = sc.inverse_transform(predicted) # 将预测结果保存到Excel文件中 df.iloc[3000:...

3、根据skleam的数据集库datasets提供的红酒数据集选择合适的算法进行聚类分析。其中，将整个数据集随机划分训练集(70%) 和测试集(30%)，聚成3类，并预测 X=[14.23.1710,2.430,15.60.127.0.2.800,3.060,0.28,2.290,5.640.1 04,3.92,1065]的类型。小

X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test) # 使用K-means算法将数据聚成3类 kmeans = KMeans(n_clusters=3, random_state=42) kmeans.fit(X_train) # 预测新数据 X_new = [[14.23, 1.71...

WinRAR_v6.11_x64_SC_Repack.exe

sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test)

from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test)

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X_test = sc.transform([[0.03237, 0.0, 2.18, 0, 0.458, 6.998, 45.8, 6.0622, 3, 222, 18.7, 394.63, 2.94]])报错

学生成绩预测模型_逻辑回归实战练习——根据学生成绩预测是否被录取，使用sigmoid函数

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