  In traditional machine learning techniques, the degree of closeness between
true and predicted values generally measures the quality of predictions.
However, these learning algorithms do not consider prescription problems where
the predicted values will be used as input to decision problems. In this paper,
we efficiently leverage feature variables, and we propose a new framework
directly integrating predictive tasks under prescriptive tasks in order to
prescribe consistent decisions. We train the parameters of predictive algorithm
within a prescription problem via bilevel optimization techniques. We present
the structure of our method and demonstrate its performance using synthetic
data compared to classical methods like point-estimate-based, stochastic
optimization and recently developed machine learning based optimization
methods. In addition, we control generalization error using different penalty
approaches and optimize the integration over validation data set.
