microsoft / LightGBM

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[R-package] confusing error when using lgb.train() with an unknown metric

2

jameslamb commented Oct 25, 2020

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

[R-package] use keyword arguments for all internal function calls

6

Open

[python] suppress the warning about categorical feature override

3

Find more good first issues →

catboost / catboost

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Custom c++ metric Run Problem

gavrenkov commented Jun 17, 2020

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],

Suggestion for Catboost Tutorial

8

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Implement ColumnDescriptionClass

1

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susanli2016 / Machine-Learning-with-Python

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Python code for common Machine Learning Algorithms

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biolab / orange3

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💡 Orange: Interactive data analysis https://orange.biolab.si

Updated Nov 7, 2020
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Trusted-AI / adversarial-robustness-toolbox

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Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference

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tirthajyoti / Machine-Learning-with-Python

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Practice and tutorial-style notebooks covering wide variety of machine learning techniques

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justmarkham / DAT8

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General Assembly's 2015 Data Science course in Washington, DC

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kk7nc / Text_Classification

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Text Classification Algorithms: A Survey

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edyoda / data-science-complete-tutorial

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For extensive instructor led learning

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alvinwan / neural-backed-decision-trees

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Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

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Updated Jun 17, 2020
Python

RGF-team / rgf

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Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers.

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fukuball / fuku-ml

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Simple machine learning library / 簡單易用的機器學習套件

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jmartinezheras / 2018-MachineLearning-Lectures-ESA

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Machine Learning Lectures at the European Space Agency (ESA) in 2018

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mdabros / SharpLearning

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Machine learning for C# .Net

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dmitryikh / leaves

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pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks

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Updated May 3, 2020
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arnaldog12 / Machine_Learning

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Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.

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dlab-berkeley / Machine-Learning-in-R

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Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles

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Updated Sep 26, 2020
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serengil / chefboost

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A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4,5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting (GBDT, GBRT, GBM), Random Forest and Adaboost w/categorical features support for Python

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Updated Jul 14, 2020
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jayshah19949596 / Machine-Learning-Models

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Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means

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Skylark0924 / Machine-Learning-is-ALL-You-Need

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🌟《Machine Learning 格物志》: ML + DL + RL basic codes and notes by sklearn, PyTorch, TensorFlow, Keras & the most important, from scratch!💪 This repository is ALL You Need!

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Updated Oct 18, 2020
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Machine Learning with the NSL-KDD dataset for Network Intrusion Detection

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RuleBasedIntegration / Rubi

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Include usage message of utility functions into the `Steps` display

halirutan commented Dec 12, 2018

Once #15 is implemented and we have a ::usage message for the integration-step condition function, this information should be included in the Steps display. I imagine for instance a tooltip when the user hovers over the green functions like IGtQ or GtQ

The implementation of this issue will require changed in [ShowStepFormatt

michaeldorner / DecisionTrees

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Seminar work "Decision Trees - An Introduction" with presentation, seminar paper, and Python implementation

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psaris / funq

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Source files for "Fun Q: A Functional Introduction to Machine Learning in Q"

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Real time face landmarking using decision trees and NN autoencoders

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IBCNServices / GENESIM

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An innovative technique that constructs an ensemble of decision trees and converts this ensemble into a single, interpretable decision tree with an enhanced predictive performance

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SilverDecisions / SilverDecisions

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Software for creating and analyzing decision trees.

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MBKraus / Predicting_real_estate_prices_using_scikit-learn

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Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

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Python

neurodata / SPORF

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This is the implementation of Sparse Projection Oblique Randomer Forest

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soaicbe / ai_all_resources

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A curated list of Best Artificial Intelligence Resources

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Updated Oct 24, 2020
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decision-trees

Here are 1,282 public repositories matching this topic...

microsoft / LightGBM

[R-package] confusing error when using lgb.train() with an unknown metric

How you are using LightGBM?

Environment info

Reproducible example(s)

[R-package] use keyword arguments for all internal function calls

[python] suppress the warning about categorical feature override

catboost / catboost

Custom c++ metric Run Problem

Suggestion for Catboost Tutorial

Implement ColumnDescriptionClass

susanli2016 / Machine-Learning-with-Python

biolab / orange3

Trusted-AI / adversarial-robustness-toolbox

tirthajyoti / Machine-Learning-with-Python

justmarkham / DAT8

kk7nc / Text_Classification

edyoda / data-science-complete-tutorial

alvinwan / neural-backed-decision-trees

RGF-team / rgf

fukuball / fuku-ml

jmartinezheras / 2018-MachineLearning-Lectures-ESA

mdabros / SharpLearning

dmitryikh / leaves

arnaldog12 / Machine_Learning

dlab-berkeley / Machine-Learning-in-R

serengil / chefboost

jayshah19949596 / Machine-Learning-Models

Skylark0924 / Machine-Learning-is-ALL-You-Need

CynthiaKoopman / Network-Intrusion-Detection

RuleBasedIntegration / Rubi

Include usage message of utility functions into the `Steps` display

michaeldorner / DecisionTrees

psaris / funq

TomaszRewak / Face-Landmarking

IBCNServices / GENESIM

SilverDecisions / SilverDecisions

MBKraus / Predicting_real_estate_prices_using_scikit-learn

neurodata / SPORF

soaicbe / ai_all_resources

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