SGDRegressor

SGDRegressor

A model type for constructing a stochastic gradient descent-based regressor, based on MLJScikitLearnInterface.jl, and implementing the MLJ model interface.

From MLJ, the type can be imported using

SGDRegressor = @load SGDRegressor pkg=MLJScikitLearnInterface

Do model = SGDRegressor() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in SGDRegressor(loss=...).

Hyper-parameters

  • loss = squared_error
  • penalty = l2
  • alpha = 0.0001
  • l1_ratio = 0.15
  • fit_intercept = true
  • max_iter = 1000
  • tol = 0.001
  • shuffle = true
  • verbose = 0
  • epsilon = 0.1
  • random_state = nothing
  • learning_rate = invscaling
  • eta0 = 0.01
  • power_t = 0.25
  • early_stopping = false
  • validation_fraction = 0.1
  • n_iter_no_change = 5
  • warm_start = false
  • average = false