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Performance Analysis of Credit Scoring Models on Lending Club Data
Polena, Michal ; Teplý, Petr (advisor) ; Pečená, Magda (referee)
In our master thesis, we compare ten classification algorithms for credit scor- ing. Their prediction performances are measured by six different classification performance measurements. We use a unique P2P lending data set with more than 200,000 records and 23 variables for our classifiers comparison. This data set comes from Lending Club, the biggest P2P lending platform in the United States. Logistic regression, Artificial neural network, and Linear discriminant analysis are the best three classifiers according to our results. Random forest ranks as the fifth best classifier. On the other hand, Classification and regression tree and k-Nearest neighbors are ranked as the worse classifiers in our ranking. 1
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Does payoff dominance matter?: an experiment
Polena, Michal ; Cingl, Lubomír (advisor) ; Levely, Ian Vandemark (referee)
Risk dominance and Payoff dominance are considered to be the most important selection criteria in Stag-hunt games. In contrast, the main finding of Schmidt et al. (2003) is that players do not respond to changes in Payoff dominance parameter in these games. There might be, however, other explanations for results of Schmidt et al. (2003). Moreover, Dubois et al. (2011) and Battalio et al. (2001)'s experimental results suggest that sufficiently large changes in Payoff dominance parameter may play a role. We, therefore, proposed three Stag-hunt games in order to examine whether players respond to large changes in Payoff dominance parameter. Furthermore, we tested the predictive power of Relative riskiness. Our main finding is that even large changes in Payoff dominance parameter do not induce players to change their choices. An in- significant trend in players' choices, caused by Relative riskiness, was detected in our second finding. Possible explanations are discussed.
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