I'd like to implement my own regressor/classificator and possibly use it in a
pipeline.
do I need to implement all methods below or can some of them be missing?
decision_function<http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor.decision_function>(X)
Predict using the linear model
densify<http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor.densify>()
Convert coefficient matrix to dense array format.
fit<http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor.fit>(X,
y[, coef_init, intercept_init, ...]) Fit linear model with Stochastic
Gradient Descent.
fit_transform<http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor.fit_transform>(X[,
y]) Fit to data, then transform it.
get_params<http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor.get_params>([deep])
Get parameters for this estimator.
partial_fit<http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor.partial_fit>(X,
y[, sample_weight]) Fit linear model with Stochastic Gradient Descent.
predict<http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor.predict>(X)
Predict using the linear model
score<http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor.score>(X,
y[, sample_weight]) Returns the coefficient of determination R^2 of the
prediction.
set_params(*args, **kwargs)
sparsify<http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor.sparsify>()
Convert coefficient matrix to sparse format.
transform<http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor.transform>(X[,
threshold]) Reduce X to its most important features.
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