2.1 读取数据 filename = 'data.csv' dataset = pd.read_csv(filename, names=names, delim_white...
ggplot()+ geom_point(aes(dataset$Level, dataset$Salary), color = "blue")+ geom_line(aes(dataset$Level, predict(svr.model, dataset)), color = "red") 好了,我们的SVR模型看起来拟合得还不错,不过最后一个观测远远大于预测值,我们可以调整模型各种参数来使曲线拟合。但是在这里,我们就暂且将这个观测...
device = 'cuda' if torch.cuda.is_available() else 'cpu' dataset = Raotbl6Dataset(X_train, y_train) dataloader = DataLoader(dataset,batch_size=32, shuffle=True) criterion = torch.nn. MSELoss() model = Model(8).to(device) opt = optim.Adam(model.parameters(), lr=LEARNING_RATE, betas...
import pandas as pd dataset = pd.read_csv('/home/mw/input/svrdemo9201/Demo data.CSV') X = dataset.iloc[:, 1:2].values Y = dataset.iloc[:, 2].values 2. 对数据进行处理,并对数据进行标准化 In [13]: X = np.reshape(X, (-1, 1)) Y = np.reshape(Y, (-1, 1)) from sklea...
y = dataset.iloc[:, 2].values 1. 2. 3. 4. 5. 6. 7. 8. 接下来,处理数据: # Reshape your data either using array.reshape(-1, 1) if your data has a single feature # array.reshape(1, -1) if it contains a single sample. ...
data = dataset, type = 'eps-regression', kernel = 'radial') 再预测下 6.5Level 的薪资 6.5Level 的薪资177861 再将数据可视化下 library(ggplot2) ggplot() + geom_point(aes(x = dataset$Level, y = dataset$Salary), colour = 'red') + ...
从用dataset1预测的精度看,除个别物资外,预测精度也都可达到85%。由于此部分物资的历史数据较少,且有很多新使用物资,模型学习效果较差,用dataset2无法对其中一部分物资进行有效预测,因此我们可将用dataset1预测的结果作为这部分物资的最终预测结果。 [0188]
# Fitting SVR to the datasetfromsklearn.svmimportSVR regressor = SVR(kernel ='rbf') regressor.fit(X, y)# Predicting a new resulty_pred = regressor.predict(sc_X.transform(np.array([[6.5]])))# 转换回正常预测值y_pred = sc_y.inverse_transform(y_pred) ...
LR模型也就是逻辑回归模型，作为一个简单的常用的模型，其有非常多的优点，除了模型简单，容易实现分布式...
#4.refit:default=True, Refit(再次拟合)the best estimator with the entire dataset #5.cv : int, cross-validation generator 此处表示3折交叉验证 gs = GridSearchCV(clf,parameters,verbose=2,refit=True,cv=3) #执行单线程网格搜索 gs.fit(X_train,y_train) ...