python 实例 SVM SVR cv 核函数 LinearSVR、RBFSampler、 SGDRegressor和 Nystroem的使用,程序员大本营,技术文章内容聚合第一站。
linearsvr = LinearSVR() # 调用LinearSVR()函数 linearsvr.fit(x_train,y_train) x = ((data[feature] - data_mean[feature])/data_std[feature]).values # 预测,并还原结果。 data['y_pred'] = linearsvr.predict(x) * data_std['y'] + data_mean['y'] outputfile = 'E:/PY1/new_reg_d...
data_train= (data_train - data_mean)/data_std#数据标准化x_train = data_train[feature].values#属性数据y_train = data_train['y'].values#标签数据linearsvr= LinearSVR()#调用LinearSVR()函数linearsvr.fit(x_train,y_train) x= ((data[feature] - data_mean[feature])/data_std[feature]).val...
一、灰度预测+LinearSVR 1、数据中显示有多种影响财政收入的因素,因此需要先筛选出影响相关性最大的因素。 1importpandas as pd2importnumpy as np3fromsklearn.linear_modelimportLasso45inputfile ='D:\ZNsmueven\Python/data.csv'#输入的数据文件6data = pd.read_csv(inputfile)#读取数据78lasso = Lasso(100...
一、灰度预测+LinearSVR import pandas as pd import numpy as np from sklearn.linear_model import Lasso inputfile = './data/data.csv' # 输入的数据文件 data =