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Sklearn has a TON of estimators, metrics, and cv iterators that could trivially be added to the `xcessiv.presets` package. I'm a bit focused on other issues to bother adding them all.
Anyone who ca…
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Thanks for a fine piece of software! It would be super neat if one were able to have the estimators predict the top _k_ tag sequences for some input, in other words to retrieve several candidates.
I…
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Since the package is not structured with a `setup.py` it is hard to install and consume. We would like to be able to install it like so:
```
python -m pip install git+https://github.com/VowpalWabbit…
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I'm looking mainly at Poisson QMLE right now, but I think this holds for several of the maximum likelihood estimators we have if the model is not parametrically correctly specified and I assume GLM to…
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getting started with a wishlist for outlier robust multivariate location and scatter estimators
#3220 size (overall scaling)
- MCD in scikit-learn, not good with high contamination and large k_vars
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Here's the code I'm using:
```
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import RandomizedSearchCV
import time
X = np.random.randn(10…
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### Describe the issue
Some of the estimators that use joblib for parallelization use process-based backend, while other use threads-based backend. Ideally, we want this to be a parameter tunable b…
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add a partially robust sandwich estimator for clusters or GEE
similar #3495 it is only misspecification robust to part of the covariance
I didn't look at details, my context is regularized, penali…
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```
import xgboost as xgb
import numpy as np
import m2cgen as m2c
from sklearn.datasets import load_boston
# train a model and save it as c code with m2cgen
X, y = load_boston(return_X_y=True)…
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Hi @kbattocchi ,
I used the following code to calculate the ATE for my panel data (around ~$18).
dml = DynamicDML(model_y=outcome_model,
model_t=treatment_model,
…