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