Procedure for the simulation of the advances in EGE from mathematics is considered. For some tasks the important predictors are obtained. The models of binary logistics regression and ordinal regression for the prediction of probabilities of solution of task are built.
Combination of the offered volume on the REPO auction with the Bank of Russia and the demand for it produce powerful signaling mechanism for the interbank money market. In order to have a possibility to emit an unintended signal there is a need for a robust estimator of the demand. This paper proposes an approach to produce such an estimator through an ensemble of logistic and linear regression models. This estimator successfully emulates many of the key features of the process.
In this study a CHAID-based approach to detecting classification accuracy heterogeneity across segments of observations is proposed. This helps to solve some important problems, facing a model-builder: (1) How to automatically detect segments in which the model significantly underperforms? and (2) How to incorporate the knowledge about classification accuracy heterogeneity across segments to partition observations in order to achieve better predictive accuracy? The approach was applied to churn data from the UCI Repository of Machine Learning Databases. By splitting the data set into four parts, which are based on the decision tree, and building a separate logistic regression scoring model for each segment we increased the accuracy by more than 7 percentage points on the test sample. Significant increase in recall and precision was also observed. It was shown that different segments may have absolutely different churn predictors. Therefore such a partitioning gives a better insight into factors influencing customer behavior.