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