EnsembleModel¶
-
class
mastml.models.EnsembleModel(model, n_estimators, **kwargs)[source]¶ Bases:
sklearn.base.BaseEstimator,sklearn.base.TransformerMixinClass used to construct ensemble models with a particular number and type of weak learner (base model). The ensemble model is compatible with most scikit-learn regressor models and KerasRegressor models
- Args:
model: (str), string name denoting the name of the model type to use as the base model
n_estimators: (int), the number of base models to include in the ensemble
kwargs: keyword arguments for the base model parameter names and values
- Methods:
- fit: method that fits the model parameters to the provided training data
- Args:
X: (pd.DataFrame), dataframe of X features
y: (pd.Series), series of y target data
- Returns:
- fitted model
- predict: method that evaluates model on new data to give predictions
- Args:
X: (pd.DataFrame), dataframe of X features
as_frame: (bool), whether to return data as pandas dataframe (else numpy array)
- Returns:
- series or array of predicted values
- get_params: method to output key model parameters
- Args:
- deep: (bool), determines the extent of information returned, default True
- Returns:
- information on model parameters
Methods Summary
fit(X, y)get_params([deep])Get parameters for this estimator. predict(X[, as_frame])Methods Documentation