If you update your LGBM version, you will get. _imports import. Since it’s supported decision tree algorithms, it splits the tree leaf wise with the simplest fit whereas other boosting algorithms split the tree depth wise. LightGBM,Release4. 9 KBLightGBM and RF differ in the way the trees are built: the order and the way the results are combined. only used in goss, the retain ratio of large gradient. Run the following command to train on GPU, and take a note of the AUC after 50 iterations: . Leagues. Contribute to GeYue/AMEX-Pred development by creating an account on GitHub. That is because we can still overfit the validation set, CV. A tag already exists with the provided branch name. Key features explained: FIFA 20. When training, the DART booster expects to perform drop-outs. history 2 of 2. Lgbm dart: 尝试解决gbdt中过拟合的问题: drop_seed: 选择dropping models 的随机seed uniform_dro: 如果你想使用uniform drop设置为true, xgboost_dart_mode: 如果你想使用xgboost dart mode设置为true, skip_drop: 在boosting迭代中跳过dropout过程的概率背景. Input. This should be initialized outside of your call to ``record_evaluation()`` and should be empty. class darts. We evaluate DART on three di er-ent tasks: ranking, regression and classi cation, using large scale, publicly available datasets. # build the lightgbm model import lightgbm as lgb clf = lgb. This will overwrite any objective parameter. I am trying to train a lightgbm ML model in Python using rmsle as the eval metric, but am encountering an issue when I try to include early stopping. We note that both MART and random for- A forecasting model using a linear regression of some of the target series’ lags, as well as optionally some covariate series lags in order to obtain a forecast. {"payload":{"allShortcutsEnabled":false,"fileTree":{"fft_lgbm/data":{"items":[{"name":"lgbm_fft_0. 5, type = double, constraints: 0. 7963|Improved. import lightgbm as lgb from numpy. In this case like our RandomForest example we will be using imagery exported from Google Earth Engine. Composability: LightGBM models can be incorporated into existing SparkML Pipelines, and used for batch, streaming, and serving workloads. ai LIghtGBM (goss + dart) + Parameter Tuning Python · Predicting Outliers to Improve Your Score, Elo_Blending, Elo Merchant Category Recommendation Source code for darts. LGBM also supports GPU learning and thus data scientists are widely using LGBM for data science application development. LightGBM is part of Microsoft's DMTK project. There are however, the difference in modeling details. Therefore, LGBM-based HL assessment model can be used as an intelligent tool to predict people’s HL levels, which can decrease greatly manual calculations. 788) 대용량 데이터를 사용하기에 적합 10000개 이하의 데이터 사용시 과적합이 일어나기 때문에 소규모 데이터 셋에는 적절하지 않음 boosting 파라미터를 dart 로 설정해주는 LGBM dart 모델이 가장 많이 쓰이면서 좋은 결과를 보여줌 (0. 3. 0. com; 2qimeng13@pku. BoosterParameterBase type DartBooster = class inherit BoosterParameterBase DART. Itisdesignedtobedistributed andefficientwiththefollowingadvantages:. GBDT (Gradient Boosting Decision Tree,勾配ブースティング決定木)のなかで最近人気のアルゴリズムおよびフレームワークのことです。. random_state (Optional [int]) – Control the randomness in. ipynb","path":"AMEX_CALIBRATION. I have to use a higher learning rate as well so it doesn't take forever to run. 3. . class darts. 1. early stopping and averaging of predictions over models trained during 5-fold cross-valudation improves. LightGBM: A Highly Efficient Gradient Boosting Decision Tree Guolin Ke 1, Qi Meng2, Thomas Finley3, Taifeng Wang , Wei Chen 1, Weidong Ma , Qiwei Ye , Tie-Yan Liu1 1Microsoft Research 2Peking University 3 Microsoft Redmond 1{guolin. 0. The library also makes it easy to backtest. DART: Dropouts meet Multiple Additive Regression Trees. pd_DataFramendarray. That said, overfitting is properly assessed by using a training, validation and a testing set. sum (group) = n_samples. Connect and share knowledge within a single location that is structured and easy to search. If one parameter appears in both command line and config file, LightGBM will use the parameter from the command line. Output. Abstract. used only in dart. I am trying to use boosting DART on my problem, but, when I choose DART instead of gbdt, DART takes forever to run a single iter. Welcome to LightGBM’s documentation! LightGBM is a gradient boosting framework that uses tree based learning algorithms. LGBMClassifier( n_estimators=1250, num_leaves=128, learning_rate=0. LightGBM is an open-source gradient boosting framework that based on tree learning algorithm and designed to process data faster and provide better accuracy. 3300 정도 나왔습니다. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. read_csv ('train_data. Input. In this piece, we’ll explore. 004786, "end_time": "2022-08-07T15:12:24. tune. used only in dart; max number of dropped trees during one boosting iteration <=0 means no limit; skip_drop ︎, default = 0. In the end block of code, we simply trained model with 100 iterations. Step: 2- Set data to function, the data which have to send back from the. py. white, inc の ソフトウェアエンジニア r2en です。. The sklearn API for LightGBM provides a parameter-. Definition Remarks Applies to Definition Namespace: Microsoft. LightGBM,Release4. Kaggle などのデータ分析競技を取り組んでいる方であれば、LightGBM（読み：ライト・ジービーエム）に触れたことがある方も多いと思います。. index. 0 and later. 8k. ML. It is designed to be distributed and efficient with the following advantages: Faster training speed and higher efficiency. test objective=binary metric=auc. 99 LightGBMisagradientboostingframeworkthatusestreebasedlearningalgorithms. NumPy 2D array (s), pandas DataFrame, H2O DataTable’s Frame, SciPy sparse matrix. Comparing daal4py inference performance to XGBoost (top) and LightGBM (bottom). 0 and it can be negative (because the model can be arbitrarily worse). It will not add any trees to the model. columns):. I extracted features of X data using Tsfresh and try to apply LightGBM algorithm to classify the data into 0(Bad) and 1(Good). microsoft / LightGBM Public. The number of trials is determined by the number of tuning parameters and also the range. LightGBM binary file. 649714", "exception. In the next sections, I will explain and compare these methods with each other. LightGBM: A Highly Efficient Gradient Boosting Decision Tree Guolin Ke 1, Qi Meng2, Thomas Finley3, Taifeng Wang , Wei Chen 1, Weidong Ma , Qiwei Ye , Tie-Yan Liu1 1Microsoft Research 2Peking University 3 Microsoft Redmond 1{guolin. Random Forest. ipynb","contentType":"file"},{"name":"AMEX. You can find all the information about the API in. You’ll need to define a function which takes, as arguments: your model’s predictions. Explore and run machine learning code with Kaggle Notebooks | Using data from Store Item Demand Forecasting ChallengeAmex LGBM Dart CV 0. . 8. When growing on an equivalent leaf, the leaf-wise algorithm optimizes the target function more efficiently than the level-wise algorithm and leads to better classification accuracies,. p ( int) – Order (number of time lags) of the autoregressive model (AR). train (), you have to construct one of these beforehand with lgb. For example, in your case, although iteration 34 is best, these trees are changed in the later iterations, as dart will update the previous trees. The example below, using lightgbm==3. Darts is a Python library for user-friendly forecasting and anomaly detection on time series. cv. 3. Advantages of LightGBM through SynapseML. My train and test accuracies are 87% & 82% respectively with cross-validation of 89%. 1 vote. Multioutput predictive models: Explaining multiclass classification and multioutput regression. train, package = "lightgbm")This function implements a sensible hyperparameter tuning strategy that is known to be sensible for LightGBM by tuning the following parameters in order: feature_fraction. The reason will be displayed to describe this comment to others. Parameters Quick Look. American Express - Default Prediction. 让我们一步一步地创建一个自定义度量函数。 定义一个单独. 354 lines (307 sloc) 13. 近年、XGBoostと並んでKaggleの上位ランカーがこぞって使うLightGBMの基本的な使い方や仕組み、さらにXGBoostとの違いに. For example, some models work on multidimensional series, return probabilistic forecasts, or accept other. 009, verbose=1 ) Using the LGBM classifier, is there a way to use this with GPU these days?After creating the necessary dataset, we created a python dictionary with parameters and their values. 5-0. max_depth : int, optional (default=-1) Maximum tree depth for base. マイクロソフトの方々が開発されています。. Better accuracy. This implementation comes with the ability to produce probabilistic forecasts. Both of them provide you the option to choose from — gbdt, dart, goss, rf (LightGBM) or gbtree, gblinear or dart (XGBoost). 8 reproduces this behavior. autokeras, catboost, lightgbm) Introduction to the dalex package: Titanic. phi = np. LightGBM,Release4. I have used early stopping and dart with no issues for the past couple months on multiple models. 调参策略：搜索，尽量不要太大。. 1. lightgbm (), on the other hand, can accept a data frame, data. 0. def record_evaluation (eval_result: Dict [str, Dict [str, List [Any]]])-> Callable: """Create a callback that records the evaluation history into ``eval_result``. Python · American Express - Default Prediction, Amex LGBM Dart CV 0. In general, the techniques used below can be also be adapted for other forecasting models, whether they be classical statistical. ]). In searching. XGBoost: A more traditional method for gradient boosting. stratifiedkfold 5fold. Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this siteThe difference between the outputs of the two models is due to how the out result is calculated. It uses some of the target series’ lags, as well as optionally some covariate series lags in order to obtain a forecast. 7977, The Fine Art of Hyperparameter Tuning +3. Dataset (). early_stopping (stopping_rounds, first_metric_only = False, verbose = True, min_delta = 0. Source code for optuna. time() from sklearn. 다중 분류, 클릭 예측, 순위 학습 등에 주로 사용되는 Gradient Boosting Decision Tree (GBDT) 는 굉장히 유용한 머신러닝 알고리즘이며, XGBoost나 pGBRT 등 효율적인 기법의 설계를 가능하게. linear_regression_model. It contains a variety of models, from classics such as ARIMA to deep neural networks. 0 <= skip_drop <= 1. Better accuracy. To use LGBM in python you need to install a python wrapper for CLI. XGBModel (lags = None, lags_past_covariates = None, lags_future_covariates = None, output_chunk_length = 1, add_encoders = None, likelihood = None, quantiles = None,. The model will train until the validation score doesn’t improve by at least min_delta. Activates early stopping. Simple LGBM (boosting_type = DART)Simple LGBM 실제 잔여대수보다 높게 예측해버리면 실제로 사용자가 거치소에 갔을때 예측한 값보다 적어서 타지 못한다면 오히려 불만이 더 커질것으로 예상했습니다. Teams. lightgbm. プロ契約したら回った。モデルをdartに変更 dartにはearly_stoppingが効かないので要注意。学習中に落ちないようにPCの設定を変更しました。 2022-07-07: 相関係数が高い変数の削除をしておきたい あとは: 2022-07-10: 変数の削除したら精度下がったので相関係数は. Now we are ready to start GPU training! First we want to verify the GPU works correctly. This means the optimal value for num_leaves lies within the range (2^3, 2^12) or (8, 4096). No branches or pull requests. DART booster (Dropouts meet Multiple Additive Regression Trees) public sealed class DartBooster : Microsoft. ndarray. Both best iteration and best score. 3. LightGBM binary file. train valid=higgs. 这次尝试修改这个模型的第二层的时候，结果得分比xgboost更高，有可能是因为在作为分类层，xgboost需要人工去选择权重的变化，而LGBM可以根据实际. data_idx – Index of data, 0: training data, 1: 1st validation data, 2. Datasets included with the R-package. Lgbm dart: 尝试解决gbdt中过拟合的问题: drop_seed: 选择dropping models 的随机seed uniform_dro: 如果你想使用uniform drop设置为true, xgboost_dart_mode: 如果你想使用xgboost dart mode设置为true, skip_drop: 在boosting迭代中跳过dropout过程的概率背景. Lower memory usage. In. and your logloss was better at round 1034. Kaggle でよく利用されているGBDT (Gradient Boosting Decision Tree)の一種. Learn more about TeamsThe reason is when using dart, the previous trees will be updated. ke, taifengw, wche, weima, qiwye, tie-yan. アンサンブルに使用する機械学習モデルは、lightgbm. I'm not sure what's wrong with my code, but the script returns the same score with different parameters, which shouldn't be happening. Both xgboost and gbm follows the principle of gradient boosting. predict_proba(test_X). Light GBM: A Highly Efficient Gradient Boosting Decision Tree 논문 리뷰. models. 本記事では以下のサイトを参考に、全4つの時系列ケースでそれぞれのモデルを適応し、時系列予測モデルをつくっています。. Author. This puts more focus on the under trained instances without changing the data distribution by much. 2. ADDITIVE and trend_mode = Trend. early_stopping lightgbm. Installing the CRAN Package; Installing from Source with CMake; Installing a GPU-enabled Build; Installing Precompiled Binarieslikelihood (Optional [str]) – Can be set to quantile or poisson. All the notebooks are also available in ipynb format directly on github. The reason is when using dart, the previous trees will be updated. 2021. The documentation does not list the details of how the probabilities are calculated. UserWarning: Starting from version 2. . Note: internally, LightGBM uses gbdt mode for the first 1 / learning_rate iterations class darts. LightGBMModel ( lags = None , lags_past_covariates = None , lags_future_covariates = None , output_chunk_length = 1. num_leaves. forecasting. 2. LightGBM. params[boost_alias] == 'dart') for boost_alias in ('boosting', 'boosting_type', 'boost')) Copy link Collaborator. schedulers import ASHAScheduler from ray. Notebook. The following table contains the subset of hyperparameters that are required or most commonly used for the Amazon SageMaker LightGBM algorithm. conf data=higgs. We don’t know yet what the ideal parameter values are for this lightgbm model. Logs. 2, type=double. LightGBM was faster than XGBoost and in some cases. what is the standard order to call lgbm functions and train models the 'lgbm' way? X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0. 并返回. It contains an array of models, from standard statistical models such as ARIMA to…tss = TimeSeriesSplit(3) folds = tss. LightGBM uses additional techniques to. Explore and run machine learning code with Kaggle Notebooks | Using data from Elo Merchant Category Recommendation2 Answers. Since it’s supported decision tree algorithms, it splits the tree leaf wise with the simplest fit […] Forecasting models are models that can produce predictions about future values of some time series, given the history of this series. Light GBM: A Highly Efficient Gradient Boosting Decision Tree 논문 리뷰. Python API is a comprehensive guide to the Python interface of LightGBM, a gradient boosting framework that uses tree-based learning algorithms. #LightGBMとはLightGBMとは決定木とアンサンブル学習のブースティングを組み合わせた勾配ブ…. Regression model based on XGBoost. predict. Abstract. Even If I use small drop_rate = 0. また、希望があればLightGBM分類の記事も作成しますので、コメント欄に記載いただければと思います。LGBM uses a special algorithm to find the split value of categorical features. 0. 6s . 0, scikit-learn==0. This implementation comes with the ability to produce probabilistic forecasts. from __future__ import annotations import sys from typing import TYPE_CHECKING import optuna from optuna. The name of evaluation function (without whitespace). Try dart; Try to use categorical feature directly; To deal with over. 8 and bagging_freq = 2, LGBM will sample 80 % of the training data every second iteration before training each tree. If this is unclear, then don’t worry, we. RankNet to LambdaRank to LambdaMART: An Overview 3 C = 1 2 (1−S ij)σ(s i −s j)+log(1+e−σ(si−sj)) The cost is comfortingly symmetric (swapping i and j and changing the sign of SStandalone Random Forest With XGBoost API. Comments (15) Competition Notebook. A might be some GUI component, and B is usually some kind of “model” object. SynapseML adds many deep learning and data science tools to the Spark ecosystem, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK), LightGBM and. Darts is an open-source Python library by Unit8 for easy handling, pre-processing, and forecasting of time series. They all face the same problem: finding books close to their current reading ability, reading normally (simple level) or improving and learning (difficulty level) without being. Thanks @Berriel, you gave me the missing piece of information. LIghtGBM (goss + dart) + Parameter Tuning. cv(params_with_metric, lgb_train, num_boost_round= 10, folds=folds, verbose_eval= False) cv_res. {"payload":{"allShortcutsEnabled":false,"fileTree":{"darts/models/forecasting":{"items":[{"name":"__init__. In the official example they don't shuffle the data. For LGB model, we use the dart gradient boosting (Lgbm dart) as the boosting methods to avoid over specialization problem of gradient boosted decision tree (Lgbm gbdt). forecasting. fit call: model_pipeline_lgbm. It is designed to be distributed and efficient with the following advantages: Faster training speed and higher efficiency. – in dart, it also affects normalization weights of dropped trees • num_leaves, default=31, type=int, alias=num_leaf – number of leaves in one tree • tree_learner, default=serial,. Introduction to the Aspect module in dalex. . max_depth : int, optional (default=-1) Maximum tree depth for base. g. Interesting observations: standard deviation of years of schooling and age per household are important features. liu}@microsoft. Parameters-----boosting_type : str, optional (default='gbdt') 'gbdt', traditional Gradient Boosting Decision Tree. ", " ", "* Could try different models, maybe some neural network with the same features or a subset of the features and then blend with LGBM can work, in my experience blending tree models and neural network works great because they are very diverse so the boost. Of course, we could try fitting all of the time series with a single LightGBM model but we can save that for next time! Since we are just using LightGBM, you can alter the objective and try out time series classification!However a drawback of applying monotonic constraints is that we lose a certain degree of predictive power as it will be more difficult to model subtler aspects of the data due to the constraints. 7k. , models trained on all 300 series simultaneously. Many of the examples in this page use functionality from numpy. read_csv ('train_data. results = model. 0. ‘rf’,. 8. This framework specializes in creating high-quality and GPU enabled decision tree algorithms for ranking, classification, and many other machine learning tasks. The goal of this notebook is to explore transfer learning for time series forecasting – that is, training forecasting models on one time series dataset and using it on another. 1, the library file in distribution wheels for macOS is built by the Apple Clang (Xcode_8. metrics from sklearn. and which returns: your custom loss name. Accuracy of the model depends on the values we provide to the parameters. xgboost. LightGBM (Light Gradient Boosting Machine) LightGBM is a gradient-boosting framework based on decision trees to increase the efficiency of the model and reduces memory usage. steps ['model_lgbm']. Most DART booster implementations have a way to control this; XGBoost's predict () has an argument named training specific for that reason. LightGBM Classification Example in Python. Saved searches Use saved searches to filter your results more quickly7. The officials instructions are the following, first the prerequisites: sudo apt-get install --no-install-recommends git cmake build-essential libboost-dev libboost-system-dev libboost-filesystem-dev (For some reason, I was still missing Boost elements as we will see later)LIGHTGBM_C_EXPORT int LGBM_BoosterGetNumPredict(BoosterHandle handle, int data_idx, int64_t *out_len) . forecasting. forecasting. steps ['model_lgbm']. optuna. com; 2qimeng13@pku. 3. Output. To suppress (most) output from LightGBM, the following parameter can be set. Multioutput predictive models: Explaining multiclass classification and multioutput regression. I'm trying to train a LightGBM model on the Kaggle Iowa housing dataset and I wrote a small script to randomly try different parameters within a given range. Further explaining the LGBM output with L1/L2: The top 5 important features are same in both the cases (with/without regularization), however importance values after top 2 features has been shrunk significantly by the L1/L2 regularized model and after top 5 features the regularized model makes importance values as good as zero (Refer images of. rf, Random Forest,. group : numpy 1-D array Group/query data. LightGBM is a gradient-boosting framework based on decision trees to increase the efficiency of the model and reduces memory usage. datasets import. You can find the details of the algorithm and benchmark results in this blog article by Kohei. py. To do this, we first need to transform the time series data into a supervised learning dataset. Our simulation experiments are based on Python programmes installed on a Windows operating system with Intel Xeon CPU E5-2620 @ 2 GHz and 16. This puts more focus on the under trained instances without changing the data distribution by much. and env. Validation score needs to improve at least every. csv') X_train = df_train. Business problem: Given anonymized transaction data with 190 features for 500000 American Express customers, the objective is to identify which customer is likely to default in the next 180 days Solution: Ensembled a LightGBM 'dart' booster model with a 5-layer deep CNN. The booster dart inherits gbtree booster, so it supports all parameters that gbtree does, such as eta, gamma, max_depth etc. Formal algorithm for GOSS. start = time. This Notebook has been released under the Apache 2. uniform_drop ︎, default = false, type = bool. weighted: dropped trees are selected in proportion to weight. 'rf', Random Forest. , 2016, Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining に掲載された。. A tag already exists with the provided branch name. Introduction to the Aspect module in dalex. group : numpy 1-D array Group/query data. Learn more about TeamsIn XGBoost, trees grow depth-wise while in LightGBM, trees grow leaf-wise which is the fundamental difference between the two frameworks. boosting ︎, default = gbdt, type = enum, options: gbdt, rf, dart, aliases: boosting_type, boost. An ensemble model which uses a regression model to compute the ensemble forecast. model_selection import train_test_split df_train = pd. Light Gradient Boosted Machine, or LightGBM for short, is an open-source library that provides an efficient and effective implementation of the gradient boosting algorithm. lgbm gbdt（梯度提升决策树）. gorithm DART. LightGBM is a distributed and efficient gradient boosting framework that uses tree-based learning. best_iteration). **kwargs –. This technique can be used to speed up training [2]. scikit-learn 0. Let’s start by installing Sktime and importing the libraries!! pip install sktime==0. ML. 6403635848830754_loss. Booster. Parameters: handle – Handle of booster. A forecasting model using a linear regression of some of the target series’ lags, as well as optionally some covariate series lags in order to obtain a forecast. agaricus. Both best iteration and best score. edu. The source code is below: def predict_proba (self, X, raw_score=False, start_iteration=0, num_iteration=None, pred_leaf=False, pred_contrib=False, **kwargs. Yes, if rate_drop=0, we effectively have zero drop-outs so are using a "standard" gradient booster machine. subsample must be set to a value less than 1 to enable random selection of training cases (rows). View Dartsvictoria. Weighted training. Thanks @Berriel, you gave me the missing piece of information. E. For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the. drop ('target', axis=1)A Tale of Three Classes¶. and optimizes their performance. LightGBM Sequence object (s) The data is stored in a Dataset object. American-Express-Credit-Default. ‘dart’, Dropouts meet Multiple Additive Regression Trees. model_selection import GridSearchCV import lightgbm as lgb lgb=lgb. Explore and run machine learning code with Kaggle Notebooks | Using data from multiple data sources7만 ai 팀이 협업하는 데이터 사이언스 플랫폼. This means that in case of installing LightGBM from PyPI via the ` ` pip install lightgbm ` ` command, you don ' t need to install the gcc compiler anymore. Interesting observations: standard deviation of years of schooling and age per household are important features. _imports import. Many of the examples in this page use functionality from numpy. The forecasting models can all be used in the same way, using fit () and predict () functions, similar to scikit-learn. Permutation Importance를 사용하여 Feature Selection. 2. **kwargs –. ke, taifengw, wche, weima, qiwye, tie-yan. 2 Answers. 2 Answers. Trina Gulliver This page was last edited on 21. split(X_train) cv_res_gen = lgb. rsample::vfold_cv(v = 5) Create a model specification for lightgbm The treesnip package makes sure that boost_tree understands what engine lightgbm is, and how the parameters are translated internaly. from __future__ import annotations import sys from typing import TYPE_CHECKING import optuna from optuna. 17. 65 from the hyperparameter tuning along with 100 estimators, Number of leaves are taken 25 with minimum 05 data in each. 7, numpy==1. , the number of times the data have had past values subtracted (I). XGBoost reigned king for a while, both in accuracy and performance, until a contender rose to the challenge. pyplot as plt import. 近年、XGBoostと並んでKaggleの上位ランカーがこぞって使うLightGBMの基本的な使い方や仕組み、さらにXGBoostとの違いに. plot_split_value_histogram (booster, feature). 따릉이 사용자들의 불편 요소를 줄이기 위해서 정확도가 조금은. g. The last boosting stage or the boosting stage found by using ``early_stopping`` callback. We have updated a comprehensive tutorial on introduction to the model, which you might want to take. The LightGBM Python module can load data from: LibSVM (zero-based) / TSV / CSV format text file. It just updates the leaf counts and leaf values based on the new data. 1 on Python 3. lightgbm. Parameters. 2. See [1] for a reference around random forests. Here is my code: import numpy as np import pandas as pd import lightgbm as lgb from sklearn. It allows the weak categorical (with low cardinality) to enter to some trees, hence better. Continued train with input GBDT model.