Predicting the Unobserved : A statistical analysis of missing data techniques for binary classification
The aim of the thesis is to investigate how the classification performance of random forest and logistic regression differ, given an imbalanced data set with MCAR missing data. The performance is measured in terms of accuracy and sensitivity. Two analyses are performed: one with a simulated data set...
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| Format: | Others |
| Language: | English |
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Uppsala universitet, Statistiska institutionen
2019
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388581 |