8.1.14 Model Types

AutoML supports the following training models. When training AutoML a model, use the ML_TRAIN model_list and exclude_model_list options to specify the training models to consider or exclude. The Model Metadata includes the algorithm_name field, which defines the model type.

Classification Models

• LogisticRegression

• GaussianNB

• DecisionTreeClassifier

• RandomForestClassifier

• XGBClassifier

• LGBMClassifier

• SVC

• LinearSVC

• ExtraTreesClassifier

Regression Models

• DecisionTreeRegressor

• RandomForestRegressor

• LinearRegression

• LGBMRegressor

• XGBRegressor

• SVR

• LinearSVR

• ExtraTreesRegressor

Forecasting Models

Univariate endogenous models:

• NaiveForecaster

• ThetaForecaster

• ExpSmoothForecaster

• ETSForecaster

• STLwESForecaster: STLForecast with ExponentialSmoothing substructure

• STLwARIMAForecaster: STLForecast with ARIMA substructure

Univariate endogenous with exogenous models:

• SARIMAXForecaster

• OrbitForecaster

Multivariate endogenous with exogenous models:

• VARMAXForecaster

Univariate or multivariate endogenous with exogenous models:

• DynFactorForecaster

Anomaly Detection Models

• GkNN: Generalized kth Nearest Neighbors

• PCA: Principal Component Analysis

• GLOF: Generalized Local Outlier Factor

Recommendation Models

Recommendation models that rate users or items to use with explicit feedback:

• Baseline

• CoClustering

• NormalPredictor

• SlopeOne

• Matrix factorization models:

  • SVD

  • SVDpp

  • NMF

Recommendation models that rank users or items to use with implicit feedback:

• BPR: Bayesian Personalized Ranking from Implicit Feedback

• CTR: Collaborative Topic Regression