Simple and crude understanding and implementation of machine learning linear regression (six): Linear regression api re-introduced --sklearn.linear_model.LinearRegression (fit_intercept = True)

Linear Regression

learning target

• Ownership in the implementation process of linear regression
• Application LinearRegression or SGDRegressor achieve regression prediction
• We know the assessment criteria and formulas regression algorithm
• Overfitting know the causes and solutions underfitting
• We know principle ridge regression and linear regression differences
• Application Ridge achieve regression prediction
• Application joblib achieve saving and loading models

2.6 linear regression api re-introduced

• sklearn.linear_model.LinearRegression(fit_intercept=True)
  • By optimizing the normal equation
  • fit_intercept: whether the calculated offset
  • LinearRegression.coef_: regression coefficient
  • LinearRegression.intercept_：偏置
• sklearn.linear_model.SGDRegressor(loss=“squared_loss”, fit_intercept=True, learning_rate =‘invscaling’, eta0=0.01)
  • SGDRegressor class implements a stochastic gradient descent learning, it supports different loss function and the regularization penalty terms to fit linear regression model.
  • loss: loss type
    • loss = "squared_loss": ordinary least squares
  • fit_intercept: whether the calculated offset
  • learning_rate : string, optional
    • Learn filling rate
    • 'Constant', and = eta0
    • ’optimal’: eta = 1.0 / (alpha * (t + t0)) [default]
    • ‘invscaling’: eta = eta0 / pow(t, power_t)
      • power_t = 0.25: the presence of the parent class which
    • For a learning rate constant value, it can be used learning_rate = 'constant', and the learning rate used to specify eta0.
  • SGDRegressor.coef_: regression coefficient
  • SGDRegressor.intercept_: Bias

sklearn API available to us two implementations, can choose to use