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gradient-boosting

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jameslamb
jameslamb commented Sep 29, 2019

One unit test in the R package is currently broken. Steps to reproduce on Mac

export CXX=/usr/local/bin/g++-8 CC=/usr/local/bin/gcc-8
Rscript build_r.R
cd R-package/tests
Rscript testthat.R

This results in the following error at the ends of the logs

[LightGBM] [Info] Saving data to binary file /var/folders/xq/wktq4zdx4jd3qdpk34d28m940000gn/T//RtmpiY1DzV/lgb.Dataset_1555
AlejandroBaron
AlejandroBaron commented Apr 28, 2020

I don't see any documentation on how to sort the variables in the plot. I don't want them sorted by shap value but in the original order, since i'm studying the behaviour of a Wave and I want to see a multiclass distribution of shap values over time

Currently they appear as

t=24
t=32
t=1
....

I want them to appear as
t=1
t=2
t=3
t=4
...

Is it possible?

dmyersturnbull
dmyersturnbull commented May 1, 2020

This is an awesome library, thanks @ddbourgin!!

Users might not know the best way to install this package and try it out. (I didn't, so I eventually just copied the source files.)
Neither the readme nor readthedocs have install instructions.

I couldn't find it on PyPi or Anaconda, and there doesn't appear to be a pyproject.toml, setup.cfg, setup.py, or conda recipe.

Moreover, the t

matheus-asilva
matheus-asilva commented Nov 27, 2019

When running
from interpret import show
from interpret.perf import ROC
blackbox_perf = ROC(blackbox_model.predict_proba).explain_perf(X_test, y_test, name='Blackbox')
show(blackbox_perf)

I have the following error

RuntimeError: Could not find open port.
Consider calling interpret.set_show_addr(("127.0.0.1", 7001)) first..

Even calling the set_show_addr, I

ghk829
ghk829 commented May 30, 2019

I run this code

import os
os.environ['is_test_suite']="True" # this is writen due to bug for multiprocessing and pickling I issued. #426 
from auto_ml import Predictor
from auto_ml.utils import get_boston_dataset
from auto_ml.utils_models import load_ml_model

# Load data
df_train, df_test = get_boston_dataset()

# Tell auto_ml which column is 'output'
# Also note columns t
awesome-decision-tree-papers
awesome-gradient-boosting-papers

python实现GBDT的回归、二分类以及多分类,将算法流程详情进行展示解读并可视化,庖丁解牛地理解GBDT。Gradient Boosting Decision Trees regression, dichotomy and multi-classification are realized based on python, and the details of algorithm flow are displayed, interpreted and visualized to help readers better understand Gradient Boosting Decision

  • Updated Jun 15, 2019
  • Python

Demo on the capability of Yandex CatBoost gradient boosting classifier on a fictitious IBM HR dataset obtained from Kaggle. Data exploration, cleaning, preprocessing and model tuning are performed on the dataset

  • Updated Dec 5, 2019
  • Jupyter Notebook
BoostedFactorization

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