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#### Summary:
Currently testing a python module wrapping https://github.com/diyabc/abcranger : posterior methodologies (model choice and parameter estimation) with Random Forests on reference table.
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**Is your feature request related to a problem? Please describe.**
Yes, the feature is related to the problem of detecting anomalies in the cryptocurrency market. The volatile nature of cryptocurrenc…
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Hi,
Tree SHAP seems to work great on boosted tree models like XGBoost. But after reading the paper on [Consistent feature attribution for tree ensembles](https://arxiv.org/abs/1706.06060) I'm wonde…
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Hi there,
Is there any plan to add prediction interval (range prediction) to the RandomForecastRegressor library? I think that will be a super useful (and necessary) addition!
Thanks,
Chen
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Hi!
This is more of a question rather than an issue. How would I use the generalized random forests to obtain propensity scores for my sample? Any examples or references?
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### Describe the workflow you want to enable
Currently the RFR implementation is only capable of handling dense multi-task problems is there any scope to change the underlying algorithm to handle Na…
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The xgboost, RF and NN models all have different ways to handle imbalanced classification datasets by using class-specific weights in their loss functions; but we currently only support this for NN mo…
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We will implement random forests for 4th edition: see https://en.wikipedia.org/wiki/Random_forest
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- Objective: Predict the duration of taxi trips in NYC accurately.
- Data: Utilize a dataset containing information such as pickup/dropoff locations, timestamps, distance, and passenger count.
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