Jaskowski¶
The class which implements the Jaskowski’s approach [1].
| Parameters | model : object, optional (default=sklearn.linear_model.LogisticRegression)
The classification model which will be used for predict uplift.
|
Methods¶
| fit(self, X, y, t) | Build the model from the training set (X, y, t). |
| predict(self, X, t=None) | Predict an uplift for X. |
fit(self, X, y, t)¶
Build the 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¶
- A Literature Survey and Experimental Evaluation of the State-of-the-Art in Uplift Modeling: A Stepping Stone Toward the Development of Prescriptive Analytics by Floris Devriendt, Darie Moldovan, and Wouter Verbeke
from pyuplift.transformation import Jaskowski
...
model = Jaskowski()
model.fit(X[train_indexes, :], y[train_indexes], t[train_indexes])
uplift = model.predict(X[test_indexes, :])
print(uplift)