Questions tagged [lightgbm]
The lightgbm tag has no usage guidance.
49
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15 views
Why does Catboost outperform other boosting algorithms?
I have noticed while working with multiple datasets that catboost with its default parameters tends to outperform lightgbm or xgboost with its default parameters even on a tabular dataset with no ...
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1answer
21 views
Hyperparameter tuning with Bayesian-Optimization
I'm using LightGBM for the regression problem and here is my code.
...
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0answers
10 views
Gbm prediction distribution different to training data
I’m doing a regression on a dataset using lightgbm. For the training data the response variable has a non normal distribution which is multimodal. However, the predictions out-of-fold have are ...
1
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1answer
22 views
Does Gradient Boosting perform n-ary splits where n > 2?
I wonder whether algorithms such as GBM, XGBoost, CatBoost, and LightGBM perform more than two splits at a node in the decision trees? Can a node be split into 3 or more branches instead of merely ...
1
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1answer
71 views
Negative R2_score Bad predictions for my Sales prediction problem using LightGBM
My project involves trying to predict the sales quantity for a specific item across a whole year. I've used the LightGBM package for making the predictions. The params I've set for it are as follows:
<...
0
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0answers
81 views
how to choose importance_type in lgbm.plot_importance
I have made a binary-classifier using lgbm.The classifier is made on unbalanced dataset.
I wanted to see the importance features of the model. There are two types of selecting importance_type -
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30 views
Feature Selection before modeling with Boosting Trees
I have read in some papers that the subset of features chosen for a boosting tree algorithm will make a big difference on the performanceso I've been trying RFE, Boruta, Clustering variables, ...
1
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1answer
20 views
How to specify scale_pos_weight value at runtime in Hyperopt?
I want to use LighgbmClassifier for a binary Classification. for Hyper Parameter tuning I want to use Hyperopt. The Dataset is imbalanced. Using Sklearns class_weight.compute_class_weight as shown ...
0
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1answer
79 views
training gradient boosting algorithm in python testing in Golang
What are the best strategy to train and save a gradient boosting algorithm, e.g. LightGBM or XGboost or Catboost in Python but load the model in GoLang and make prediction with Golang ?
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100 views
Understanding interplay between eval_metric, metric and first_metric_only parameters in LGBMClassifier
In python API of LGBMClassifier, the constructor takes parameters metric and first_metric_only. Their descriptions are as ...
0
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2answers
247 views
handling missing values for LightGBM model
I have read that LightGBM handles missing values defaultly. And there certain parameters to change the consideration of missing values like zero_as_missing etc..,
I have seen some people using ...
0
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1answer
148 views
Correct theoretical regularized objective function for XGB/LGBM (regression task)
I am writing an academic paper on the application of Machine Learning methods to Time Series Forecasting and I am unsure about how to write down the theoretical part about the regularized objective ...
2
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1answer
57 views
How can i tell if my model is overfitting from the distribution of predicted probabilities?
all,
i am training light gradient boosting and have used all of the necessary parameters to help in over fitting.i plot the predicted probabilities (i..e probabililty has cancer) distribution from the ...
1
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1answer
742 views
Lightgbm confidence interval
I want to compute a confidence interval for each sample for a lightgbm model I've trained.
If the model was a random forest, it'd be quite easy, just take all the trees and compute the standard ...
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0answers
35 views
XGBOOST/lLightgbm over-fitting despite no indication in cross-validation test scores?
We currently work on a project where we aim to identify a set of predictors that may influence the risk of a relatively rare outcome. We are using a semi-large clinical dataset, with data on nearly ...
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0answers
38 views
Does zero_as_missing parameter affects categorical features? LightGBM
Dealing with categorical features while training LightGBM model implies encoding them as integers and providing categorical_feature parameter with their indices or names.
LightGBM documentation says ...
7
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2answers
1k views
What is the proper way to use early stopping with cross-validation?
I am not sure what is the proper way to use early stopping with cross-validation for a gradient boosting algorithm. For a simple train/valid split, we can use the valid dataset as the evaluation ...
0
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1answer
27 views
Should we do Feature selection in parallel with feature engineering?
I'm working with LightGBM on a large data set about 3M row and about 8 columns. When i ...
3
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1answer
768 views
How is the “base value” of SHAP values calculated?
I'm trying to understand how the base value is calculated. So I used an example from SHAP's github notebook, Census income classification with LightGBM.
Right after I trained the lightgbm model, I ...
2
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1answer
670 views
How to use r2-score as a loss function in LightGBM?
I am trying to implement a custom loss function in LightGBM for a regression problem. The intrinsic metrics do not help me much, because they penalise for outliers... Is there any way to use ...
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0answers
132 views
Gradient boosting Regression with zero-inflated outcome
I am trying to tune a Regression gradient boosting model where my target variable is zero inflated (80% zero) and the rest of the values are distributed as positive and negative values (not necessary ...
3
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1answer
517 views
Small number of estimators in gradient boosting
I am tuning a regression gradient boosting-based model to determine the appropriate hyperparameters using 4-folds cross validation. More specifically, I am using XGBoost and lightGBM for the models ...
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1answer
48 views
0
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1answer
365 views
random forest and log_loss metric?
Light gbm has the metric with log_loss for binary or multi classification.
is Random Forest also has the loss function with log_loss?
1
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1answer
84 views
Number of leaves for lightgbm is smaller than categories in one feature
I was looking at a notebook someone posted for a Kaggle competition. They use lightgbm with the number of leaves set to 40. If I understand right, that's setting a limit on the size of the weak ...
3
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1answer
427 views
What does the repeated message “No further splits with positive gain, best gain: -inf” mean?
I am training a LightGBM classifier on a binary classification problem.
From time to time I get the following message repeatedly:
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4
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1answer
2k views
How do GBM algorithms handle missing data?
How do algorithms GBM algorithms, such as XGBoost or LightGBM handle NaN values?
I know that they learn how to replace NaN values with other values but my question is: How do they do it exactly?
2
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2answers
370 views
Correct interpretation of summary_plot shap graph
While through the various resources online to understand the shap plots, I ended up slightly confused. Find below my interpretation of the overall plot given in examples -
Shap value 0 for a feature ...
2
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1answer
132 views
Extremely high gain with LightGBM
I am working on a binary classification problem. The target variable is not linearly separable, so I've decided to use LightGBM with default parameters (I only play with n_estimators on range from 10 -...
5
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1answer
688 views
Math Behind GOSS (Gradient-Based One Side Sampling)?
As per my understanding through books & Google Search,
GOSS (Gradient-Based One Side Sampling) is a novel sampling method that downsamples the instances on the basis of gradients. As we
know ...
5
votes
1answer
561 views
What is Pruning & Truncation in Decision Trees?
Pruning & Truncation
As per my understanding
Truncation: Stop the tree while it is still growing so that it may not end up with leaves containing very low data points. One way to do this is to set ...
6
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1answer
3k views
Random Forest VS LightGBM
Random Forest VS LightGBM
Can somebody explain in-detailed differences between Random Forest and LightGBM? And how the algorithms work under the hood?
As per my understanding from the documentation:
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-1
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2answers
1k views
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0answers
419 views
Read back a saved LGBMClassifier model
I trained a LGBMClassifier model and saved in a file in this way:
...
2
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1answer
981 views
LightGBM vs Sklearn LightGBM- Mistake in Implementation- Exact same parameters giving different results
While passing the exact same parameters to LightGBM and sklearn's implementation of LightGBM, I am getting different results. Initially, I was getting the exact same results on doing this, however, I ...
5
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2answers
726 views
SHAP value analysis gives different feature importance on train and test set
Should SHAP value analysis be done on the train or test set?
What does it mean if the feature importance based on mean |SHAP value| is different between the train and test set of my lightgbm model?
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0answers
320 views
Use LightGBM or FFM - imbalanced dataset
I have a highly imabalanced dataset but one that is not sparse. In train there are 1328 positives out of 104000. In validation ...
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1answer
58 views
LightGBM choice of evaluation metric
I have past data of a large number of people who applied for a loan and their movement through 8 different stages, from start of application to loan being paid out.
I am trying to build a model that ...
0
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1answer
128 views
Sliding window approach using SVR & LightGBM
I'm working on a multivariate time series forecast using a couple of ML algorithms (Neural Networks, Support Vector Machines & Gradient boosting algorithms). I need to measure the performance of ...
0
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1answer
930 views
How to choose the model parameters (RandomizedSearchCV, .GridSearchCV) or manually
Faced with the task of selecting parameters for the lightgbm model, the question accordingly arises, what is the best way to select them? I used the RandomizedSearchCV method, within 10 hours the ...
2
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0answers
145 views
Comparing feature importance in LightGBM + Scikit
I have a model trained using LightGBM (LGBMRegressor), in Python, with scikit-learn.
On a weekly basis the model in re-trained, and an updated set of chosen features and associated ...
10
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2answers
7k views
L1 & L2 Regularization in Light GBM
This question pertains to L1 & L2 regularization parameters in Light GBM. As per official documentation:
reg_alpha (float, optional (default=0.)) – L1 ...
3
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2answers
2k views
Light GBM Regressor, L1 & L2 Regularization and Feature Importances
I want to know how L1 & L2 regularization works in Light GBM and how to interpret the feature importances.
Scenario is: I used LGBM Regressor with RandomizedSearchCV (cv=3, iterations=50) on a ...
0
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1answer
395 views
Hyperparameter Tuning Time Series in Production
I have a time series data that handled using GDBT to predict the next value. I always use previous 30 days data to train daily, but overtime the data to predict and train is increased because the ...
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0answers
399 views
How to save a lightGBM model that updates predictions after each fold?
Hi When I use gradient boosting on Kaggle and large data sets, I run a code like this:
...
0
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1answer
31 views
Syntax of the inputs for LightGBM binary build (without python API)
I built the LightGBM binaries from https://github.com/microsoft/LightGBM. The build was successful, but the python API did not work for me. I am using the light gbm binary directly to run for time ...
5
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1answer
4k views
Differences between class_weight and scale_pos weight in LightGBM
I have a very imbalanced dataset with the ratio of the positive samples to the negative samples being 1:496. The scoring metric is the f1 score and my desired model is LightGBM. I am using the sklearn ...
2
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2answers
7k views
How to make LightGBM to suppress output?
I'm trying for a while to figure out how to "shut up" LightGBM. Especially, I want to suppress the output of LightGBM during training (the feedback on the boosting steps).
My model:
...
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0answers
310 views
How does L1 Loss work in lightGBM
From the paper, lightGBM does a subsampling according to sorted $|g_i|$, where $g_i$ is the gradient (for the loss function) at a data instance.
My question is that, when the objective is L1 loss/...
