使用sklearn的线性回归进行预测

1 回答

• 1

  import matplotlib.pyplot as plt
  import numpy as np
  from sklearn import linear_model
  from sklearn.cross_validation import train_test_split
  
  #create x data
  data2['xraw'] = data2.index
  x  = data2['xraw'].astype(np.int64) // 10**9
  
  y  = data2['kwh']
  y = y.reshape((y.shape[0],1))
  
  #train-test split
  x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)
  
  
  # Create linear regression object
  regr = linear_model.LinearRegression()
  
  # Train the model using the training sets
  regr.fit(x_train, y_train)
  
  
  # The coefficients
  print('Coefficients: \n', regr.coef_)
  
  # The mean square error
  print("Residual sum of squares: %.2f" % np.mean((regr.predict(x_test) - y_test) ** 2))
  
  # Explained variance score: 1 is perfect prediction
  print('Variance score: %.2f' % regr.score(x_test, y_test))
  
  # Plot outputs
  plt.scatter(x_test, y_test,  color='black')
  plt.plot(x_test, regr.predict(x_test), color='blue', linewidth=3)
  
  plt.xticks(())
  plt.yticks(())
  
  plt.show()
  

  回复于 2024-05-05T15:24:36+08:00