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Looking into error of possible different agg models for ensemble purposes
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main thing that works: training and testing on the 13 data!
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Possible questions that it brings
- What is its performace on lower dimensional data?
- how to transfer this into lower dimensional data?
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Eror Analysis:
- the main postion of the error comes from the recall.
- Q: why do I have this much error in the recall? And how to decrease knowing that we only take 4% recall?
- An option maybe to feed the data into a graph! why graph you ask? Or models that take into account a dependence between all the data that we have at the moment of the prediction
- the main postion of the error comes from the recall.
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sample 50-50 data from the dataset for taining (did not improve the performance)
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the cat model with the simplest agg model -> using the cat variables as a cont var with LabelEncoder
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use the sum of the cat features and create and train a cat model
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ivnestigate possible different aggregations
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run the lightgbm, xgb with a variaty of different features
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SFA SFA suggests that some feature may be more informative when transformed. Currently investigating combinations of features
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What does the results of SFA imply?
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possibly come up with a distribution of Weak learners sampling from the dist of sinfle factor analysis
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Different precisons from the official pytorch
- I have a convolutional model that works pretty good for the C13
- I know that recall is the main bottleneck in its error metric
- I have started training weak models. -> a possible path is to train so many weak models give it to a ensemble learner that inegrates the preds
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train SFA
- on c13 data
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evaluate errors
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later propogate the results into the rest of the dataset
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Add the predictions from other models to the features that you have. Initial attmepts with XGB did not perform the best! This is the way to go..
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Investigate ranked probabilities
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ContCols + Cat cols transformed -> 793 (could possibly have been improved with CV and early stopping)
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K79 + 6 best 13 overfits significantly with 799 on validation data and somehow 785 on real test data!!
Main finding here: Different features result in a different complementary predictions - CNN with SFA
- consider using the max_bin_by_feature of the lightgbm
How do we combine all the predictions (that are complementory into each other) into a final prediction
How do we add a prediction from one model into the other for complementory predictions?