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Currently, `SymbolicRegressor` returns a model that better complies with a certain criteria. This, however, is computed on the training set. Machine learning best practices dictate that model selectio…
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Try to remove or merge non-standard features upstream. Initial list:
- [ ] cereal needed? > check current xgboost (de)serialization portability: 32/64-bit, big/little-endian?
- [ ] serialization …
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Short version:
{code:python}
/usr/local/lib/python3.6/dist-packages/h2o/model/metrics_base.py in auc(self)
191 def auc(self):
192 """The AUC for this set of metrics."""
--> …
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2021-05-11 20:25:33,653 [INFO]: Call netMHCpan
2021-05-11 20:25:56,931 [INFO]: Begin Neoantign Score Process ...
2021-05-11 20:25:57,004 [INFO]: Epit number is: 130
2021-05-11 20:26:02,848 [WARN…
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I was trying to run the sample code of convert_xgboost(onnxmltools/docs/examples/plot_convert_xgboost.py), and I got an error ["AssertionError: Missing required property "tree_info".] which is only fo…
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hello I am, running this code and I see the error shown above
library(modelplotr)
scores_and_ntiles
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understand xgboost and gradient boosted trees, then explain to the team members as if theyre five years old
http://xgboost.readthedocs.io/en/latest/model.html
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Xgboost supports categorical features since 1.6 but I am stumbling into an error when using it in shapicant. Here is a minimal example
```
import pandas as pd
import numpy as np
from shapicant i…
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Goal: create a new module (for now say: `cleanlab.experimental.training_dynamics`) that allows users to provide model outputs/info at every iteration (aka checkpoint) of an iteratively trained model (…
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I try to train a XGBoost model with a GPU instance (P3.16xlarge), but it reports error:
```
OSError: Job with key $03017f000001c68bffffffff$_908b7c8320882d5230bc213e87c44498 failed with an exception…