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Building on Part 1 of the project, and your understanding of the classifiers you have implemented, you are to demonstrate your understanding by validating the choice of hyper-parameters of each algori…
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## Feature request
### Tool(s) or class(es) involved
_StructuralVariationDiscoveryPipelineSpark, XGBoostEvidenceFilter.java_
### Description
Currently there are two unresolved large structural…
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From your `bay_rcc_example.ipynb`, you learnt your adjacency matrix from error_df. Could you elaborate on how you construct error_df and how you generate it from the raw data?
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[Right for the right reasons: Training differentiable models by constraining their explanations](https://www.ijcai.org/proceedings/2017/371)
Expressive classifiers such as neural networks are among t…
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I'm seeing that the order of the dataset being used matters while calculating the shapley values. Is that the case ?
Sample code
```python
import numpy as np
import pandas as pd
import xgboost…
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so, finally, we came to the end of the project here are a few links to each and everything we have used, hope it might be useful :)
**feature extractors**
LNIP(feature extractor) I am preparing th…
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For now, there is this repo where you can grep up to date API in form of router paths and `params.get` (or `query.get` or maybe something else I haven't seen) which access keyword parameters, but this…
hcpl updated
2 months ago
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Dear Nick,
I'm sorry for asking this question here but i tried to reach the http://www.nickgillian.com/forum/ forum but it's not working.
My question is about the following article
http://www.nickgi…
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**I'm submitting a ...**
- [X] bug report
- [ ] feature request
- [X] question about the decisions made in the repository
- [x] suggestion
**Describe the bug. What is the current behavior?**
…
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[Streaming weak submodularity: Interpreting neural networks on the fly](https://papers.nips.cc/paper/6993-streaming-weak-submodularity-interpreting-neural-networks-on-the-fly)
In many machine learnin…