How you are using LightGBM?

LightGBM component: R package

Environment info

Operating System: macOS 10.14

C++ compiler version: gcc 8.1.0

CMake version: 3.17.3

R version: 4.0.2

LightGBM version or commit hash: https://github.com/microsoft/LightGBM/tree/c07644d1d71540204a9b56f26667e8180bd009e2

Reproducible example(s)

Thanks to @Laurae2 for sharing this with m

Not a high-priority at all, but it'd be more sensible for such a tutorial/testing utility corpus to be implemented elsewhere - maybe under /test/ or some other data- or doc- related module – rather than in gensim.models.word2vec.

Originally posted by @gojomo in RaRe-Technologies/gensim#2939 (comment)

Problem:
catboost version: 0.23.2
Operating System: all
Tutorial: https://github.com/catboost/tutorials/blob/master/custom_loss/custom_metric_tutorial.md

Impossible to use custom metric (С++).

Code example

from catboost import CatBoost
train_data = [[1, 4, 5, 6],
 

Add .nextElementSibling and .previousElementSibling properties to DOM Element that point to sibling elements.

• nextElementSibling
• previousElementSibling

I'm using latest pyod version on pypi. How to generate simulated data where x-axis is time? Thank you.

for reproducibility, all classifiers should be seeded by random_state. In our experiments, the random_state is set to the resampleID. This is not correctly set in all the considered classifiers. The current list to check is

all_classifiers = [
"BOSSEnsemble", "TemporalDictionaryEnsemble", "MUSE", "WEASEL",
"ProximityForest", "ElasticEnsemble", "KNeighborsTimeSeriesClassifier",
#"Shap