macrosynergy.learning.forecasting.naive_predictors#
- class NaiveRegressor[source]#
Bases:
BaseEstimator,RegressorMixinEqually weighted unbiased factor model.
Notes
Given a collection of factors that are theoretically positively correlated with a dependent variable, a plausible signal is a simple average of those factors. This is effectively a linear regression model with zero intercept and equal weights for all factors.
This is a useful benchmark model which works well when the factors are as uncorrelated as possible with one another, because it offers a layer of diversification on the underlying return drivers. When the user has strong priors, this is often a competitive model that is difficult to beat.
However, it is vital for the features to have been preprocessed to have a positive theoretical correlation with the target variable.
- fit(X, y=None)[source]#
Fit method.
- Parameters:
X (pd.DataFrame, pd.Series or np.ndarray) – The input feature matrix.
y (pd.DataFrame, pd.Series or np.ndarray) – The target variable.
- Returns:
The fitted model.
- Return type:
self
Notes
This method involves fully trusting one’s priors and thus requires no learning element. As a consequence, no training set information is needed.
- predict(X)[source]#
Predict method.
Notes
The predictions are simply the average of the features across columns of the input feature matrix.
- set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') NaiveRegressor#
Configure whether metadata should be requested to be passed to the
scoremethod.Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True(seesklearn.set_config()). Please check the User Guide on how the routing mechanism works.The options for each parameter are:
True: metadata is requested, and passed toscoreif provided. The request is ignored if metadata is not provided.False: metadata is not requested and the meta-estimator will not pass it toscore.None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.