All Questions
5 questions
0
votes
0
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44
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Model with no historical data
I need to develop a new credit default classification model for which there are a lot of features available but very few historical data (because it's a new activity launched by the company I work for)...
- 181
2
votes
1
answer
2k
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Calibrating probability thresholds for multiclass classification
I have built a network for the classification of three classes. The network consists of a CNN followed by two fully-connected layers. The CNN consists of convolutional layers, followed by batch ...
- 246
1
vote
0
answers
53
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calibrating classifier probabilities for unbalanced data when class ratios are unknown
I've built a binary classification convolutional neutral network, trained on simulated data with equal numbers of simulations for each class. I've obtained good results for a validation set with equal ...
- 111
3
votes
1
answer
59
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I have 3 graphs of a binary Logistic Regression that I want to understand better what is happening and learn of a strategy to make the model better
My problem is the following: I have a binary Logistic Regression model with a very imbalanced dataset that outputs the percentage of the prediction. As can be seen in the images, as the threshold is ...
5
votes
2
answers
4k
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convert predict_proba results using class_weight in training
As my dataset is unbalanced(class 1: 5%, class 0: 95%) I have used class_weight="balanced" parameter to train a random forest classification model. In this way I penalize the misclassification of a ...
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