EnsembleModel

class mastml.models.EnsembleModel(model, n_estimators, **kwargs)[source]

Bases: BaseEstimator, TransformerMixin

Class 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

fit(X, y)[source]
get_params(deep=True)[source]

Get parameters for this estimator.

Parameters

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsdict

Parameter names mapped to their values.

predict(X, as_frame=True)[source]