zepid.superlearner.estimators.EmpiricalMeanSL¶

class
zepid.superlearner.estimators.
EmpiricalMeanSL
¶ Empirical mean estimator in the format of SciKit learn. This estimator is for use with the SuperLearner functionality.
Note
Generally, I do not recommend its use outside of SuperLearner. Essentially the empirical mean is a baseline estimator with which to compare other estimators included in SuperLearner.
Parameters: None – Examples
Setup the environment and data set
>>> from zepid import load_sample_data >>> from zepid.superlearner import EmpiricalMeanSL >>> df = load_sample_data(False).dropna() >>> X = np.asarray(df[['art', 'male', 'age0']]) >>> y = np.asarray(df['dead'])
EmpiricalMean estimation
>>> emp_mean = EmpiricalMeanSL() >>> emp_mean.fit(X=X, y=y)
EmpiricalMean prediction
>>> emp_mean.predict(X=X)
Methods
fit
(X, y)Estimate the empirical mean based on X and y. predict
(X)Predict the value of y given a set of X covariates. 
fit
(X, y)¶ Estimate the empirical mean based on X and y. While X in input, it has no effect on the estimated empirical mean (since the empirical mean is the average for the full y).
Parameters:  X (numpy.array) – Training data
 y (numpy.array) – Target values
Returns: Return type: None

predict
(X)¶ Predict the value of y given a set of X covariates. Because X has no effect on the empirical mean, the mean from the data used in the fit() step is returned for all observations.
Parameters: X (numpy.array) – NumPy array of covariates Returns: Return type: NumPy array of predicted empirical means of the dimension X.shape[0]
