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) | Build a model from the training set (X, y, t). |
| predict(self, X, t=None) | 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¶
- 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)