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Cadit

The class which implements the cadit approach [1].

Parameters
model : object, optional (default=sklearn.linear_model.LinearRegression)
  The regression model which will be used for predict uplift.

Methods

fit(self, X, y, t) <cadit_fit> Build a model from the training set (X, y, t).
predict(self, X, t=None) <cadit_predict> Predict an uplift for X.

fit(self, X, y, t)

Build a model from the training set (X, y, t).

Parameters
X: numpy ndarray with shape = [n_samples, n_features]
  Matrix of features.
y: numpy array with shape = [n_samples,]
  Array of target of feature.
t: numpy array with shape = [n_samples,]
  Array of treatments.
Returns self : object

predict(self, X, t=None)

Predict an uplift for X.

Parameters
X: numpy ndarray with shape = [n_samples, n_features]
  Matrix of features.
t: numpy array with shape = [n_samples,] or None
  Array of treatments.
Returns
self : object
  The predicted values.

References

  1. Weisberg HI, Pontes VP. Post hoc subgroups in clinical trials: Anathema or analytics? // Clinical trials. 2015 Aug;12(4):357-64.
from pyuplift.variable_selection import Cadit
...
model = Cadit()
model.fit(X[train_indexes, :], y[train_indexes], t[train_indexes])
uplift = model.predict(X[test_indexes, :])
print(uplift)