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To expand on fuwiak's answer, you can cluster the current loan group, declare clusters to be classes, and see whether a good fraction from your default set gets classified in one of the classes/clusters. If yes, this class is predictive of default. Another take would be to do anomaly detection: use you default set to train the detector and apply it on the ...


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Assuming I can’t use external datasets, what’s the solution to this problem? How can I best understand those who have defaulted if I don’t have something to compare them to? If you wanna use this only this data as classification task, you can't perform this task. You could way this around, by generated fake data with label 0(you think about which value of ...


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I would call this bad feature engineering, I'm afraid: as the designer of a ML system, one is supposed to analyze their data and find the best way to make the ML system perform as well as possible. In this case by adding a simple feature x % 2 for every instance the decision tree can perform perfectly. [added] Even in the case of a more complex pattern, if ...


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The less data you have, the less complex your model can be. Otherwise you will overfit your data. There is not really a good way for me to judge what model is appropriate for you without knowing a lot about your dataset, but I doubt you will get anything sensible from 200 data points with a deep learning model. Try some simpler models like bag-of-words and ...


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how can I trust of my results? You probably shouldn't trust your results, because the large variation is likely caused by overfitting. Basically your model is not very reliable. My guess is that you have either too many features or not enough instances, or any combination of these two issues. I am not sure If it's correct to take the best result on the ...


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Depending on the parameters you used for your model, it may not be calibrated in probabilities. That is, your model output a score, that is helpfull to give a relative order between your instance, but the score may not reflect the real % chance of the output happening. Softmax, will at least garanty that your output are between 0 and 1 and sum to one. This ...


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The underlying model will be a stepwise function. I don't see any garanty that it will work better (or worse) with the transformation, in the general case. This may be different depending on you variable (for binary variables you may want to work directly on the linear predictor). However, in practice, if you know there is an underlying link transformation,...


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Splitting is the same your are optimising for the parameters. In other words, gini, entropy etc logic will stay the same but the breadth, width, number of features etc will be different when you optimise it with different loss functions. Accuracy, precision, F-scores etc. are evaluation metrics computed from binary outcomes and binary predictions. They are ...


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The main difference is that tensorflow is based on numerical methods (i.e., gradient descents). There is no gradient in tree-based methods. The exception is gradient regression tree.


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Good job looking at the tree and understanding what has happened. There is no problem splitting on the same feature multiple times. A continuous feature has many split points available. The tree continues to subset and refine. The split criteria shows what will be the "best" greedy split at this point. If a feature is income, perhaps the best split is \$100,...


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I would say trees are "differently" robust in this sense. A tree model will never predict a target value outside the range of those in the training set; so never a negative value for a count, or more infections than the population, etc. (Some tree-based models might, e.g. gradient boosting, but not a single tree or a random forest.) But sometimes that's ...


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There are different ways to include categorical features, and in many of them a single leaf can combine multiple categories: With the label, target, or frequency encoding the categorical feature is effectively replaced by a numeric one, so a leaf can include multiple original categories naturally. Conversely, any numeric feature can be thought of as an ...


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