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- builtins.object
-
- ForecastRegressor
- VotingClassifier
- VotingRegressor
class ForecastRegressor(builtins.object) |
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ForecastRegressor(models, model_weights=None)
Implements a forcast voting regressor.
Takes a list of fitted models and their weights and returns a weighted average of the predictions.
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Methods defined here:
- __init__(self, models, model_weights=None)
- Initialize the ForecastRegressor object.
Args:
models: list of models to be stacked
model_weights: list of weights for each model. Default is None.
- forecast(self, steps)
- Forecast the target variable using the fitted models.
Args:
steps: number of steps to forecast
Returns:
y_pred: predicted target variable
- get_params(self)
- Get the parameters of the ForecastRegressor object.
Returns:
params: dictionary of parameters
- show_models(self, formula=False)
- Print the models and their weights.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class VotingClassifier(builtins.object) |
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VotingClassifier(estimators, weights=None)
Implements a hard voting classifier.
Aggregates predictions from multiple fitted classification models based on
majority vote (optionally weighted).
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Methods defined here:
- __init__(self, estimators, weights=None)
- Initialize the VotingClassifier object for hard voting.
Args:
estimators (list): A list of *fitted* classifier objects.
Each estimator must have a `predict` method.
weights (array-like of shape (n_estimators,), optional): Sequence of
weights (float or int) to weight the occurrences of predicted class
labels during voting. Uses uniform weights if None. Defaults to None.
- get_params(self, deep=True)
- Get parameters for this estimator.
Args:
deep (bool, optional): If True, will return the parameters for this
estimator and contained subobjects that are estimators. (Not fully implemented for deep=True yet).
Returns:
params (dict): Parameter names mapped to their values.
- predict(self, X)
- Predict class labels for X using hard voting.
Args:
X (array-like of shape (n_samples, n_features)): The input samples.
Returns:
maj (np.ndarray of shape (n_samples,)): Predicted class labels based on majority vote.
- show_models(self)
- Print the models and their weights.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class VotingRegressor(builtins.object) |
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VotingRegressor(models, model_weights=None)
Implements a voting regressor.
Takes a list of fitted models and their weights and returns a weighted average of the predictions.
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Methods defined here:
- __init__(self, models, model_weights=None)
- Initialize the VotingRegressor object.
Args:
models: list of models to be stacked
model_weights: list of weights for each model. Default is None.
- get_params(self)
- Get the parameters of the VotingRegressor object.
Returns:
params: dictionary of parameters
- predict(self, X)
- Predict the target variable using the fitted models.
Args:
X: input features
Returns:
y_pred: predicted target variable
- show_models(self, formula=False)
- Print the models and their weights.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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