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Hi there, since this is one of the very few LS-SVM implementations, I thought you might be interested to know that I've recently released a new LS-SVM package called [Neo LS-SVM](https://github.com/ls…
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Hi Scott,
First off, thanks for the work you've done on SHAP. I think it'll definitely help people get more comfortable with ML model results and make them mainstream.
I came across the followin…
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# Tweet summary
Stable and not-overfitting approach to rank feature importance. Iterate feature importance test based on RandomForest or boosting methods with randomly generated additional "shadow" f…
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### Algorithm
catboost
### Package
catboost
### Supported types
* [v] classif
* [ ] clust
* [ ] dens
* [v] regr
* [ ] surv
### I have checked that this is not already implemente…
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# Tune XGBoost with tidymodels and #TidyTuesday beach volleyball | Julia Silge
Learn how to tune hyperparameters for an XGBoost classification model to predict wins and losses.
[https://juliasilge.c…
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When sample weights are negative, the probabilities can come out negative as well:
```
>>> rng = np.random.RandomState(10)
>>> X = rng.randn(10, 4)
>>> y = rng.randint(0, 2, 10)
>>> sample_weight = r…
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Currently each model that needs it defines their own losses, it might be useful to put them all in one place to see if anything could be re-used in the future.
In particular, losses are defined in,…
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# Create a synthetic dataset
y, X, treatment, _, _, _ = synthetic_data(mode=1, n=20, p=5, sigma=1.0)
ebm_treated = ExplainableBoostingClassifier(
learning_rate=0.01,
max_leaves=5,
m…
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Temperature should proportionally affect all moves (UCTNode::randomize_first_proportionally). Curently 0 visit children are not proportionally adjusted. This might be a minor issue and not even be rel…
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