The following model types are supported:

• Linear Models: LinearRegression, Ridge, Lasso, ElasticNet, LogisticRegression, etc.
• Neighbors Models: KNeighborsClassifier, KNeighborsRegressor, RadiusNeighborsClassifier, etc.
• Neural Network Models: MLPClassifier, MLPRegressor, BernoulliRBM
• SVM Models: SVC, SVR, LinearSVC, LinearSVR, NuSVC, NuSVR, OneClassSVM, etc.
• Tree Models: DecisionTreeClassifier, DecisionTreeRegressor, ExtraTreeClassifier, etc.

Each template uses the same base attributes:

• generic_field_key (str, required): Key of the generic field where datasets are stored
• model_save_path (str, required): Path where the trained model will be saved

XGBoost model templates include:

• XGBClassifier
• XGBRegressor
• XGBRanker
• XGBRFClassifier
• XGBRFRegressor
• Booster

Attributes are the same as those for Scikit-learn templates.

Templates for dimensionality reduction using scikit-learn's manifold learning techniques:

• SKLearnManifold: Base class for all manifold learning algorithms
  • generic_field_key (str, required): Key of the generic field where the input data is stored

Specific algorithms include t-SNE, MDS, Isomap, LocallyLinearEmbedding, and more.

Templates for using trained models to make predictions on new data:

• SKLearnInference: For inference with scikit-learn models
• XGBoostInference: For inference with XGBoost models

To use these templates, you should replace the model_path to point to the path of the trained model.

agent:
  name: sklearn_linear_models_agent
  description: agent to train a LinearRegression model from scikit-learn using the load_diabetes dataset

templates:
- template_name: InputTemplate
  class_name: InputTemplate
  attributes: {}

- template_name: load_diabetesWrapper
  class_name: load_diabetesWrapper
  template_input: InputTemplate
  attributes:
    split_dataset: true
    train_size: 0.8
    load_diabetes:
      return_X_y: false
      as_frame: true

- template_name: LinearRegressionWrapper
  class_name: LinearRegressionWrapper
  template_input: load_diabetesWrapper
  attributes:
    generic_field_for_data: load_diabetesWrapper
    model_save_path: "artifacts/linear_regression.joblib"
    linearregression_init:
      fit_intercept: true
      copy_X: true
      n_jobs: null
      positive: false