gbdt | A GBDT and LambdaMART training and predicting package | Machine Learning library

by zhangyafeikimi C++ Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | gbdt Summary

gbdt is a C++ library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. gbdt has no bugs, it has no vulnerabilities and it has low support. However gbdt has a Non-SPDX License. You can download it from GitHub.

A GBDT(MART) and LambdaMART training and predicting package

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gbdt has a low active ecosystem.

It has 17 star(s) with 10 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

gbdt has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of gbdt is current.

Quality

gbdt has 0 bugs and 0 code smells.

Security

gbdt has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

gbdt code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

gbdt has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

gbdt releases are not available. You will need to build from source code and install.

Installation instructions are not available. Examples and code snippets are available.

It has 658 lines of code, 53 functions and 9 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

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gbdt Key Features

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gbdt Examples and Code Snippets

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Community Discussions

Trending Discussions on gbdt

Why LightGBM with 'objective': 'binary' donot return binary value 0 and 1 when call method predict?

BayesianOptimization fails due to float error

Reproducing LightGBM's `logloss` in the Python API

ValueError: k-fold cross-validation requires at least one train/test split by setting n_splits=2 or more, got n_splits=1

Optuna lightgbm integration giving categorical features error

How to understand Shapley value for binary classification problem?

`sklearn` asking for eval dataset when there is one

Light GBM Value Error: ValueError: For early stopping, at least one dataset and eval metric is required for evaluation

LightGBMRegressor Custom eval loss fuction return as list error for single output

How to make Python LightGBM code to accept list

QUESTION

Why LightGBM with 'objective': 'binary' donot return binary value 0 and 1 when call method predict?

Asked 2022-Apr-10 at 19:29

I create a binary classification model with LightGBM:

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ANSWER

Answered 2022-Apr-10 at 19:29

train() in the LightGBM Python package produces a lightgbm.Booster object.

For binary classification, lightgbm.Booster.predict() by default returns the predicted probability that the target is equal to 1.

Consider the following minimal, reproducible example using lightgbm==3.3.2 and Python 3.8.12

Source https://stackoverflow.com/questions/71813904

QUESTION

BayesianOptimization fails due to float error

Asked 2022-Mar-21 at 22:34

I want to optimize my HPO of my lightgbm model. I used a Bayesian Optimization process to do so. Sadly my algorithm fails to converge.

MRE

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ANSWER

Answered 2022-Mar-21 at 22:34

This is related to a change in scipy 1.8.0, One should use -np.squeeze(res.fun) instead of -res.fun[0]

https://github.com/fmfn/BayesianOptimization/issues/300

The comments in the bug report indicate reverting to scipy 1.7.0 fixes this,

It seems the fix is been proposed in the BayesianOptimization package: https://github.com/fmfn/BayesianOptimization/pull/303

But this has not been merged and released yet, so you could either:

fall back to scipy 1.7.0
use the forked github version of BayesianOptimization with the patch (https://github.com/samFarrellDay/BayesianOptimization)
apply the patch in issue 303 manually on your system

Source https://stackoverflow.com/questions/71460894

QUESTION

Reproducing LightGBM's `logloss` in the Python API

Asked 2021-Aug-05 at 20:52

I want to start using custom classification loss functions in LightGBM, and I thought that having a custom implementation of binary_logloss is a good place to start. Following the answer here I managed to get a custom logloss with performance approximately identical to the builtin logloss (in the scikit-learn API).

I tried following the same logic in the Python API:

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ANSWER

Answered 2021-Aug-05 at 20:52

The differences in the results are due to:

The different initialization used by LightGBM when a custom loss function is provided, this GitHub issue explains how it can be addressed. The easiest solution is to set 'boost_from_average': False.
The sub-sampling of the features due to the fact that feature_fraction < 1. This may require opening an issue in GitHub as it is not clear why the results are not reproducible given that the feature_fraction_seed is fixed by default.

Source https://stackoverflow.com/questions/68669043

QUESTION

ValueError: k-fold cross-validation requires at least one train/test split by setting n_splits=2 or more, got n_splits=1

Asked 2021-Jul-01 at 06:17

I am getting the error

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ANSWER

Answered 2021-Jul-01 at 06:03

This error is pretty straightforward. You cannot perform a Kfold split with only 1 split.

The Kfold documentation states that n_splits is the number of folds and must be at least 2.

If you want to perform only a single split you should use sklearn.model_selection.train_test_split.

Source https://stackoverflow.com/questions/68204883

QUESTION

Optuna lightgbm integration giving categorical features error

Asked 2021-Feb-20 at 14:12

Im creating a model using optuna lightgbm integration, My training set has some categorical features and i pass those features to the model using the lgb.Dataset class, here is the code im using ( NOTE: X_train, X_val, y_train, y_val are all pandas dataframes ).

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ANSWER

Answered 2021-Feb-20 at 14:12

In case of picking the name (not indexes) of those columns, add as well the feature_name parameters as the documentation states

That said, your dval and dtrain will be initialized as follow:

Source https://stackoverflow.com/questions/66287854

QUESTION

How to understand Shapley value for binary classification problem?

Asked 2021-Feb-05 at 20:39

I am very new to shapley python package. And I am wondering how should I interpret the shapley value for the Binary Classification problem? Here is what I did so far. Firstly, I used a lightGBM model to fit my data. Something like

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ANSWER

Answered 2021-Feb-03 at 17:54

Let's run LGBMClassifier on a breast cancer dataset:

Source https://stackoverflow.com/questions/66018154

QUESTION

`sklearn` asking for eval dataset when there is one

Asked 2021-Jan-14 at 11:56

I am working on Stacking Regressor from sklearn and I used lightgbm to train my model. My lightgbm model has an early stopping option and I have used eval dataset and metric for this.

When it feeds into the StackingRegressor, I saw this error

ValueError: For early stopping, at least one dataset and eval metric is required for evaluation

Which is frustrating because I do have them in my code. I wonder what is happening? Here's my code.

...

ANSWER

Answered 2021-Jan-14 at 11:56

I guess the issue is causing by the fact that early_stopping was used in the LGBMRegressor, thus it expects eval data in StackingRegressor() as well.

Try doing the following:

Just after the line you've fitted your LGBMRegressor() model with the following line - m1.fit(X_train_df, y_train_df, eval_set = (X_val_df, y_val_df), eval_metric = 'rmse'), add these lines after that.

Source https://stackoverflow.com/questions/65713104

QUESTION

Light GBM Value Error: ValueError: For early stopping, at least one dataset and eval metric is required for evaluation

Asked 2020-Nov-29 at 03:44

Here is my code. It is a binary classification problem and the evaluation criteria are the AUC score. I have looked at one solution on Stack Overflow and implemented it but did not work and still giving me an error.

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ANSWER

Answered 2020-Nov-29 at 03:44

Answer

This error is caused by the fact that you used early stopping during grid search, but decided not to use early stopping when fitting the best model over the full dataset.

Some keyword arguments you pass into LGBMClassifier are added to the params in the model object produced by training, including early_stopping_rounds.

To disable early stopping, you can use update_params().

Source https://stackoverflow.com/questions/64974817

QUESTION

LightGBMRegressor Custom eval loss fuction return as list error for single output

Asked 2020-Nov-25 at 07:49

I want to use custom eval function in my lightGBM model. My code is as follow:

...

ANSWER

Answered 2020-Nov-24 at 16:24

Just change your return values as tuples

Source https://stackoverflow.com/questions/64989568

QUESTION

How to make Python LightGBM code to accept list

Asked 2020-Nov-09 at 10:13

I am using the folllowing code:

...

ANSWER

Answered 2020-Nov-09 at 09:19

Using labelencoder would allow to convert your column to the expected format:

Source https://stackoverflow.com/questions/64748771

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

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Install gbdt

You can download it from GitHub.

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