Project on Ensemble method

Aims: Write a program (or programs) implementing various ensemble methods (such as bagging and boosting) together with some underlying algorithms.

Background: Ensemble methods were one of the topics of the CS5100 course. Assignment 3 only scratched the surface of this important area, and this project will give the student a chance to delve deeper into this topic.

Early Deliverables

A proof-of-concept implementation of bagging, random forests, or boosting both in the case of regression and in the case of classification.

Implementing underlying algorithms, such as decision stumps or decision trees; simple proof-of-concept implementations are sufficient.

Final Deliverables

A final program implementing bagging, random forests, or boosting (or all three) with a careful choice of the parameters.

Implementations of the underlying algorithms should be complemented by a careful empirical study of the effect of various parameters; producing optimized programs.

Comparing the performance of different ensemble techniques in the problems of classification and regression.

Using bagged and boosted decision trees as probability predictors; evaluating their perfomance using proper loss functions.

Ideally, the final product will have a graphical user interface.

The final report will describe the theory behind the algorithms, the implementation issues necessary to apply the theory, the software engineering process involved in generating your software, computational experiments with different data sets, methods, and parameters.

Suggested Extensions

Implementing the out-of-bag estimate of error probability.

Investigating variable importance.

Proximity plots.

Theoretical analysis of the algorithms.

Færdigheder: Python

Om klienten:
( 0 bedømmelser ) Slough, United Kingdom

Projekt ID: #34197614

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