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It would be nice to expose the following regressors to SKLL since they can be quite useful in the real world:
linear_model.BayesianRidge Bayesian ridge regression
linear_model.ElasticNet Linear regression with combined L1 and L2 priors as regularizer.
linear_model.ElasticNetCV Elastic Net model with iterative fitting along a regularization path
linear_model.Lars Least Angle Regression model a.k.a.
linear_model.LarsCV Cross-validated Least Angle Regression model
linear_model.LassoCV Lasso linear model with iterative fitting along a regularization path
linear_model.LassoLars Lasso model fit with Least Angle Regression a.k.a.
linear_model.LassoLarsCV Cross-validated Lasso, using the LARS algorithm
linear_model.LassoLarsIC Lasso model fit with Lars using BIC or AIC for model selection
linear_model.LogisticRegressionCV Logistic Regression CV (aka logit, MaxEnt) classifier.
linear_model.RidgeCV Ridge regression with built-in cross-validation.
linear_model.lars_path Compute Least Angle Regression or Lasso path using LARS algorithm
linear_model.lasso_path Compute Lasso path with coordinate descent
linear_model.lasso_stability_path Stabiliy path based on randomized Lasso estimates
Perhaps we can future-proof this in a way so that it's easy to add new models as they are released in subsequent versions of scikit-learn?
The text was updated successfully, but these errors were encountered:
Add the default parameter grid to the _DEFAULT_PARAM_GRIDS dict. One of our main selling points is that we "put some thought" into what these should be, so this can't really be automated that much.
# Convert items to list to prevent exception about modifying while iteratingforname, class_inlist(globals().items()):
ifisinstance(class_, type) andclass_!=RegressorMixinandissubclass(class_,
RegressorMixin):
rescaled_name='Rescaled{}'.format(name)
globals()[rescaled_name] =rescaled(class_)
It would be nice to expose the following regressors to SKLL since they can be quite useful in the real world:
Perhaps we can future-proof this in a way so that it's easy to add new models as they are released in subsequent versions of scikit-learn?
The text was updated successfully, but these errors were encountered: