Print the Sci-kit linear model list

Question

Is it possible to access some kind of list of all the linear models in sklearn? They must be stored somewhere and accessible right?

Answer 1

The best way is to check their documentation, which lists all their linear models on the website. This should be the first place to look because it provides in-depth explanations and examples for each model.

Alternatively, if you want to load it in Python, use the dir function to list all the properties and methods of linear_model.

from sklearn import linear_model

print(dir(linear_model))

['ARDRegression', 'BayesianRidge', 'ElasticNet', 'ElasticNetCV', 
'GammaRegressor', 'Hinge', 'Huber', 'HuberRegressor', 'Lars', 'LarsCV',
 'Lasso', 'LassoCV', 'LassoLars', 'LassoLarsCV', 'LassoLarsIC',
'LinearRegression', 'Log', 'LogisticRegression', 'LogisticRegressionCV', 
'ModifiedHuber', 'MultiTaskElasticNet', 'MultiTaskElasticNetCV', 
'MultiTaskLasso', 'MultiTaskLassoCV', 'OrthogonalMatchingPursuit',
 'OrthogonalMatchingPursuitCV', 'PassiveAggressiveClassifier',
 'PassiveAggressiveRegressor', 'Perceptron', 'PoissonRegressor', 
'QuantileRegressor', 'RANSACRegressor', 'Ridge', 'RidgeCV', 'RidgeClassifier',
 'RidgeClassifierCV', 'SGDClassifier', 'SGDOneClassSVM', 'SGDRegressor', 
'SquaredLoss', 'TheilSenRegressor', 'TweedieRegressor', '__all__', 
'__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__',
 '__package__', '__path__', '__spec__', '_base', '_bayes', '_cd_fast', 
'_coordinate_descent', '_glm', '_huber', '_least_angle', '_linear_loss', 
'_logistic', '_omp', '_passive_aggressive', '_perceptron', '_quantile', 
'_ransac', '_ridge', '_sag', '_sag_fast', '_sgd_fast', '_stochastic_gradient',
 '_theil_sen', 'enet_path', 'lars_path', 'lars_path_gram', 'lasso_path', 
'orthogonal_mp', 'orthogonal_mp_gram', 'ridge_regression']

However, using dir returns more than just the different models, it returns all the properties and methods. Without more context from the docs, what's the difference between Ridge and ridge_regression?

Tldr; check the documentation on the website.