Supported Models

Linear Models

Model	Task	Description
linear_regression	Regression	Ordinary least squares
ridge	Regression	L2 regularization
lasso	Regression	L1 regularization (feature selection)
elasticnet	Regression	L1 + L2 regularization
logistic_regression	Classification	Linear classifier

Tree Models

Model	Task	Description
decision_tree	Both	Single decision tree

Bagging Models

Model	Task	Description
random_forest	Both	Bootstrap aggregating
extra_trees	Both	Extremely randomized trees

Boosting Models

Model	Task	Description
xgboost	Both	Gradient boosting (GPU support)
lightgbm	Both	Fast gradient boosting
gradient_boosting	Both	sklearn gradient boosting
hist_gradient_boosting	Both	Histogram-based (handles NaN)
adaboost	Both	Adaptive boosting

Instance-Based Models

Model	Task	Description
knn	Both	K-nearest neighbors
svm	Both	Support vector machine

Probabilistic Models

Model	Task	Description
naive_bayes	Classification	Gaussian naive Bayes

Ensemble Models

Model	Task	Description
stacking	Both	Stacking ensemble
voting	Both	Voting ensemble

Model Selection

open-mlpipe auto-selects models based on:

• Task type (regression/classification)
• Dataset size
• Number of features
• Data characteristics