Work in progress.
For now run this to install:
sudo apt-get install build-essential python3-dev
git clone https://github.com/psarka/uplift
cd uplift
python3.6 -m 'venv' venv
source venv/bin/activate
pip install -e .
from uplift.ensemble import RandomForestClassifier
from uplift.datasets import make_radcliffe_surry
from uplift.metrics import qini_q
X_train, y_train, group_train = make_radcliffe_surry()
X_test, y_test, group_test, uplift_test = make_radcliffe_surry(return_uplift=True)
rfc = RandomForestClassifier(n_estimators=50, min_samples_leaf=200, criterion='uplift_gini')
rfc.fit(X_train, y_train, group_train)
uplift_pred = rfc.predict_uplift(X_test)
print(qini_q(y_test, uplift_pred, group_test))1999, N.Radcliffe, P.Surry, Differential Response Analysis: Modeling True Responses by Isolating the Effect of a Single Action
2002, B.Hansotia, B.Rukstales, Incremental Value Modeling
2007, N.Radcliffe, Using Control Groups to Target on Predicted Lift: Building and Assessing Uplift Models
2010, P.Rzepakowski, S.Jaroszewicz, Decision trees for uplift modeling
2011, N.Radcliffe, P.Surry, Real-World Uplift Modelling with Significance-Based Uplift Trees
2012, P.Rzepakowski, S.Jaroszewicz, Decision trees for uplift modeling with single and multiple treatments
2015, L.Guelman, M.Guillen, M.Perez-Marin, Uplift Random Forests
2015, M.Soltys, S.Jaroszewicz, P.Rzepakowski, Ensemble methods for uplift modeling
2017, W.Verbeke, C.Bravo, B.Baesens, Profit drive business analytics: A practitioner's guide to transforming big data into added value. (Chapter 4)