Models Registry

Model Types

Provides registry functionality for Model classes. For implementation details see EligibleModels

DummyRegressorModel	Description Wrapper for sklearn.dummy.DummyRegressor. For implementation details see DummyRegressorModel Attributes TASK: regression TAGS: [‘dummy’]
DummyClassifierModel	Description Wrapper for sklearn.dummy.DummyClassifier. For implementation details see DummyClassifierModel Attributes TASK: classification TAGS: [‘dummy’]
DummySurvivalModel	Description Dummy survival model. For implementation details see DummySurvivalModel Attributes TASK: survival TAGS: [‘dummy’]
LogisticRegressionModel	Description Wrapper for sklearn.linear_model.LogisticRegression. For implementation details see LogisticRegressionModel Attributes TASK: classification TAGS: [‘linear’, ‘regularization’]
LassoModel	Description Wrapper for sklearn.linear_model.Lasso. For implementation details see LassoModel Attributes TASK: regression TAGS: [‘linear’, ‘regularization’]
ElasticNetModel	Description Wrapper for sklearn.linear_model.ElasticNet. For implementation details see ElasticNetModel Attributes TASK: regression TAGS: [‘linear’, ‘regularization’]
RidgeModel	Description Wrapper for sklearn.linear_model.Ridge. For implementation details see RidgeModel Attributes TASK: regression TAGS: [‘linear’, ‘regularization’]
LassoLarsAICModel	Description Wrapper for sklearn.linear_model.LassoLarsIC with AIC criterion. For implementation details see LassoLarsAICModel Attributes TASK: regression TAGS: [‘linear’, ‘regularization’]
LassoLarsBICModel	Description Wrapper for sklearn.linear_model.LassoLarsIC with BIC criterion. For implementation details see LassoLarsBICModel Attributes TASK: regression TAGS: [‘linear’, ‘regularization’]
BernoulliNBClassifierModel	Description Wrapper for sklearn.naive_bayes.BernoulliNB. For implementation details see BernoulliNBClassifierModel Attributes TASK: classification TAGS: [‘naive_bayes’]
KNeighborsRegressorModel	Description Wrapper for sklearn.neighbors.KNeighborsRegressor. For implementation details see KNeighborsRegressorModel Attributes TASK: regression TAGS: [‘neighbors’]
KNeighborsClassifierModel	Description Wrapper for sklearn.neighbors.KNeighborsClassifier. For implementation details see KNeighborsClassifierModel Attributes TASK: classification TAGS: [‘neighbors’]
RandomSurvivalForestModel	Description Wrapper for sksurv.ensemble.RandomSurvivalForest For implementation details see RandomSurvivalForestModel Attributes TASK: survival TAGS: [‘tree’, <ModelingTask.SURV: ‘survival’>]
CoxNetModel	Description Wrapper for sksurv.linear_model.CoxnetSurvivalAnalysis - Elastic. For implementation details see CoxNetModel Attributes TASK: survival TAGS: [‘linear’, <ModelingTask.SURV: ‘survival’>, ‘regularization’]
CoxLassoModel	Description Wrapper for sksurv.linear_model.CoxnetSurvivalAnalysis - Lasso. For implementation details see CoxLassoModel Attributes TASK: survival TAGS: [‘linear’, <ModelingTask.SURV: ‘survival’>, ‘regularization’]
CoxRidgeModel	Description Wrapper for sksurv.linear_model.CoxPHSurvivalAnalysis - Ridge. For implementation details see CoxRidgeModel Attributes TASK: survival TAGS: [‘linear’, <ModelingTask.SURV: ‘survival’>, ‘regularization’]
IPCRidgeModel	Description Wrapper for sksurv.linear_model.IPCRidge For implementation details see IPCRidgeModel Attributes TASK: survival TAGS: [‘linear’, <ModelingTask.SURV: ‘survival’>, ‘regularization’]
GradientBoostingSAModel	Description Wrapper for sksurv.ensemble.GradientBoostingSurvivalAnalysis For implementation details see GradientBoostingSAModel Attributes TASK: survival TAGS: [‘ensemble’, <ModelingTask.SURV: ‘survival’>, ‘tree’]
SurvivalSVMModel	Description Wrapper for sksurv.svm.FastSurvivalSVM Note on usage: When rank_ratio parameter is 1, only ranking is performed. When it is zero, objective is regression, so the whole semantics gets reversed. With ranking prediction is risk (the lower the better), with regression preidction is survival time (the higher the better). In this particular model rank_ratio is (ans should be) fixed to 1. For implementation details see SurvivalSVMModel Attributes TASK: survival TAGS: [‘svm’, <ModelingTask.SURV: ‘survival’>, ‘exclude’]
SurvivalKernelSVMModel	Description Wrapper for sksurv.svm.FastKernelSurvivalSVM Note on usage: When rank_ratio parameter is 1, only ranking is performed. When it is zero, objective is regression, so the whole semantics gets reversed. With ranking prediction is risk (the lower the better), with regression preidction is survival time (the higher the better). In this particular model rank_ratio is (ans should be) fixed to 1. For implementation details see SurvivalKernelSVMModel Attributes TASK: survival TAGS: [‘svm’, <ModelingTask.SURV: ‘survival’>]
SurvivalHingeLossSVMModel	Description Wrapper for sksurv.svm.HingeLossSurvivalSVM For implementation details see SurvivalHingeLossSVMModel Attributes TASK: survival TAGS: [‘svm’, <ModelingTask.SURV: ‘survival’>]
SurvivalMinlipSVMModel	Description Wrapper for sksurv.svm.MinlipSurvivalAnalysis For implementation details see SurvivalMinlipSVMModel Attributes TASK: survival TAGS: [‘svm’, <ModelingTask.SURV: ‘survival’>]
SurvivalNaiveSVMModel	Description Wrapper for sksurv.svm.NaiveSurvivalSVM For implementation details see SurvivalNaiveSVMModel Attributes TASK: survival TAGS: [‘svm’, <ModelingTask.SURV: ‘survival’>]
LinearSVCClassifierModel	Description Wrapper for sklearn.svm.LinearSVC. For implementation details see LinearSVCClassifierModel Attributes TASK: classification TAGS: [‘svm’, ‘no_predict_proba’]
SVCClassifierModel	Description Wrapper for sklearn.svm.SVC. For implementation details see SVCClassifierModel Attributes TASK: classification TAGS: [‘svm’, ‘no_predict_proba’]
SVRClassifierModel	Description Wrapper for sklearn.svm.SVR. For implementation details see SVRClassifierModel Attributes TASK: regression TAGS: [‘svm’, ‘no_predict_proba’]
LightGbmRegressorModel	Description Wrapper for LGBMRegressor. For implementation details see LightGbmRegressorModel Attributes TASK: regression TAGS: [‘tree’]
LightGbmClassifierModel	Description Wrapper for LGBMClassifier. For implementation details see LightGbmClassifierModel Attributes TASK: classification TAGS: [‘tree’]
DecisionTreeRegressorModel	Description Wrapper for sklearn.tree.DecisionTreeRegressor. For implementation details see DecisionTreeRegressorModel Attributes TASK: regression TAGS: [‘tree’]
DecisionTreeClassifierModel	Description Wrapper for sklearn.tree.DecisionTreeClassifier. For implementation details see DecisionTreeClassifierModel Attributes TASK: classification TAGS: [‘tree’]
ExtraTreesRegressorModel	Description Wrapper for sklearn.ensemble.ExtraTreesRegressor. For implementation details see ExtraTreesRegressorModel Attributes TASK: regression TAGS: [‘tree’, ‘ensemble’]
GradientBoostingRegressorModel	Description Wrapper for sklearn.ensemble.GradientBoostingRegressor. For implementation details see GradientBoostingRegressorModel Attributes TASK: regression TAGS: [‘tree’, ‘ensemble’]
RandomForestRegressorModel	Description Wrapper for sklearn.ensemble.RandomForestRegressor. For implementation details see RandomForestRegressorModel Attributes TASK: regression TAGS: [‘tree’, ‘ensemble’]
ExtraTreesClassifierModel	Description Wrapper for sklearn.ensemble.ExtraTreesClassifier. For implementation details see ExtraTreesClassifierModel Attributes TASK: classification TAGS: [‘tree’, ‘ensemble’]
GradientBoostingClassifierModel	Description Wrapper for sklearn.ensemble.GradientBoostingClassifier. For implementation details see GradientBoostingClassifierModel Attributes TASK: classification TAGS: [‘tree’, ‘ensemble’]
RandomForestClassifierModel	Description Wrapper for sklearn.ensemble.RandomForestClassifier. For implementation details see RandomForestClassifierModel Attributes TASK: classification TAGS: [‘tree’, ‘ensemble’]