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From the description of your problem, it's clear that you have: Low n/p ratio, since no. of observations are small and features are relatively high. Class imbalance, since event rate is approx. 3%. Both the cases are undesirable to any modelling procedure. You can separately address both the problems. To increase n/p ratio: "Feature selection is an ...


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First a general remark. Some datasets contain discriminative features, others much less so. It may be that all your $210$ features have very little predictive power for the classification task your are investigating. My advice for the next step is as follows. Draw at random $50\%$ cases from your $0$ category and $50\%$ at random from your $1$ category. This ...


-1

If interactions among the 210 variables are a possibility, then use C4.5 or C5.0 on a balanced dataset, as MatchMakerEE's answer suggests. Random forests could be tried next if you're unhappy with the results. If you expect no interactions among the 210 variables, or only known interactions that you can specify, instead first try factorial logistic ...


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The "procedure" outlined below is fully analogous to the one you suggest. I have chosen a simpler estimation procedure, with only one parameter, to make the computations easier. Real experiment, actual data. An urn contains 1000 red balls and 1100 green balls. The true proportion of red balls in the urn is $\theta = 10/21 = 0.4761905.$ Sampling ...


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I would start by looking at the formulas: $recall=\frac{TP}{TP+FN}$ $precision = \frac{TP}{TP+FP}$ $F_1 = 2 \frac{(precision) (recall)}{precision+recall}$ From here it is easy to see that precision and recall are inversely proportional. This means when one increases, the other one decreases. One option is to adjust your threshold and analyze your f1 score. ...


7

The question title is: How to get log odds from these results of logistic regression The estimates are already on the log-odds scale. All you have to do is read the relevant entry. What are the odds of a male surviving as compared to a female? The log-odds of a male surviving compared to a female is -2.5221, holding the other variables constant. If we ...


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You can use the Chi-2 test for $k$ independent samples to test whether your classifiers (your models for prediction of pass/fail) yield the same fractions of misclassified prediction. If $H_0$ is rejected and there are differences between the model performance estimates, you can use pairwise Chi-2 tests to test whether one prediction model with a specific ...


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I have to disagree with all of the answers. The original problem is not appropriate for classification at all but calls for an analysis of tendencies. See http://fharrell.com/post/classification Miscasting the task as a classification task is what has caused so much work for everyone, and has caused invalid statistical methods that discard valuable data to ...


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