Regression

We currently support regression algorithms from scikit-learn, XGBoost, LightGBM and Catboost.

Example

This example trains models on the sklean diabetes dataset. This example uses multiple input features to predict a single output variable.


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-- load the dataset
SELECT pgml.load_dataset('diabetes');

-- view the dataset
SELECT * FROM pgml.diabetes LIMIT 10;

-- train a simple model on the data
SELECT * FROM pgml.train('Diabetes Progression', 'regression', 'pgml.diabetes', 'target');

-- check out the predictions
SELECT target, pgml.predict('Diabetes Progression', ARRAY[age, sex, bmi, bp, s1, s2, s3, s4, s5, s6]) AS prediction
FROM pgml.diabetes 
LIMIT 10;

Algorithms

Gradient Boosting

Algorithm	Reference
`xgboost`	XGBRegressor
`xgboost_random_forest`	XGBRFRegressor
`lightgbm`	LGBMRegressor
`catboost`	CatBoostRegressor

Examples


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SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'xgboost', hyperparams => '{"n_estimators": 10}');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'xgboost_random_forest', hyperparams => '{"n_estimators": 10}');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'lightgbm', hyperparams => '{"n_estimators": 1}');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'catboost', hyperparams => '{"n_estimators": 10}');

Ensembles

Algorithm	Reference
`ada_boost`	AdaBoostRegressor
`bagging`	BaggingRegressor
`extra_trees`	ExtraTreesRegressor
`gradient_boosting_trees`	GradientBoostingRegressor
`random_forest`	RandomForestRegressor
`hist_gradient_boosting`	HistGradientBoostingRegressor

Examples


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SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'ada_boost', hyperparams => '{"n_estimators": 5}');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'bagging', hyperparams => '{"n_estimators": 5}');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'extra_trees', hyperparams => '{"n_estimators": 5}');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'gradient_boosting_trees', hyperparams => '{"n_estimators": 5}');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'random_forest', hyperparams => '{"n_estimators": 5}');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'hist_gradient_boosting', hyperparams => '{"max_iter": 10}');

Support Vector Machines

Algorithm	Reference
`svm`	SVR
`nu_svm`	NuSVR
`linear_svm`	LinearSVR

Examples


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SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'svm', hyperparams => '{"max_iter": 100}');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'nu_svm', hyperparams => '{"max_iter": 10}');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'linear_svm', hyperparams => '{"max_iter": 100}');

Linear

Algorithm	Reference
`linear`	LinearRegression
`ridge`	Ridge
`lasso`	Lasso
`elastic_net`	ElasticNet
`least_angle`	LARS
`lasso_least_angle`	LassoLars
`orthoganl_matching_pursuit`	OrthogonalMatchingPursuit
`bayesian_ridge`	BayesianRidge
`automatic_relevance_determination`	ARDRegression
`stochastic_gradient_descent`	SGDRegressor
`passive_aggressive`	PassiveAggressiveRegressor
`ransac`	RANSACRegressor
`theil_sen`	TheilSenRegressor
`huber`	HuberRegressor
`quantile`	QuantileRegressor

Examples


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SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'linear');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'ridge');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'lasso');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'elastic_net');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'least_angle');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'lasso_least_angle');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'orthogonal_matching_pursuit');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'bayesian_ridge');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'automatic_relevance_determination');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'stochastic_gradient_descent');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'passive_aggressive');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'ransac');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'theil_sen', hyperparams => '{"max_iter": 10, "max_subpopulation": 100}');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'huber');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'quantile');

Other

Algorithm	Reference
`kernel_ridge`	KernelRidge
`gaussian_process`	GaussianProcessRegressor

Examples


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SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'kernel_ridge');
SELECT * FROM pgml.train('Diabetes Progression', algorithm => 'gaussian_process');

PostgresML