Questions tagged [lightgbm]
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75
questions
0
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1answer
13 views
LightGBM eval_set - what to do when I fit the final model (there's no test data left)
I'm using LightGBM's eval_set feature when fitting my model. This enables early stopping on the number of estimators used.
...
1
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1answer
14 views
Understanding feature_parallel distributed learning algorithm in LightGBMClassifier
I want to understand feature_parallel algorithm in LightGBMClassifier.
It describes how it is done traditionally and how LightGBM...
2
votes
1answer
30 views
Example for Boosting
Can someone exactly tell me how does boosting as implemented by LightGBM or XGBoost work in real case scenerio. Like I know it splits tree leaf wise instead of level wise, which will contribute to ...
1
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0answers
8 views
Fractional Differencing/Differentiation for Non-Time based Model; Look-ahead bias?
I have time-series data, but instead of using a time-based model like RNN, I've decided to approach my classification problem using an lgbm classifier. To do so, I have modified the data, such that ...
2
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1answer
30 views
Optimizing MAE degrades MAE metrics
I have run a lighgbm regression model by optimizing on RMSE and measuring the performance on RMSE:
...
0
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1answer
48 views
LightGBM regressor score function?
I'm trying to find what is the score function for the LightGBM regressor. In their documentation page I could not find any information regarding the function used to calculate the score attribute...
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0answers
23 views
what is the meaning of split maximum return leaf, non split maximum return leaf in lightgbm
I am currently working on lightgbm, where i read this sentence i am unable to understand what this means. can anyone help me that what it means.
thank you.
https://www.hindawi.com/journals/mpe/2020/...
0
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2answers
34 views
Churn prediction model doesn't predict good on real data
I am working currently on churn prediction problem.
As an input I use data from date warehouse for a period 082016 - 032021(one row per month for each customer).
Based on this data I have created a ...
0
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0answers
15 views
For a Multi-class Classification Problem, What are the Pros and Cons of using a Cascade ML model versus Single Multi-class Classification model?
I am developing an ML model for classification using tabular data. It has 5 classes right now and new classes are expected to be continuously added. (Already have a new one leading to an imbalanced ...
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0answers
16 views
What is the best way to create input data samples using in XGBoost for predicting number of next days that customer will come back to store
I'm building the tree-based model like a XGBoost to solve the problem about customer purchase cycle.
And I think, I will build 2 models which one is predicting the customer will come back to store in ...
0
votes
1answer
12 views
Additional business rules in ensemble methods (RF, Boosted Trees)
How is it possible (if at all) to implement additional business constraints to an ensemble machine learning model, such as random forests or boosted trees?
These additional business rules can be ...
2
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0answers
249 views
Binary classification with imbalanced dataset, about lightgbm output probability distribution
I trained a binary classifier for an imbalanced dataset. I did two experiments:
lightgbm classifier, boosting_type='gbdt', objective='cross_entropy', SMOTE upsample
After training the lgbm model, I ...
2
votes
2answers
34 views
confused on "real score" vs "decision value" in classification trees
I'm reading the guide to XGBoost and am confused about the distinction it draws between the scoring systems of decision trees and classification/regression trees. The paragraph I am hung up on is:
A ...
0
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0answers
13 views
Model variance increases during training
I trained a regression model with lightgbm and the learning curve doesn't look good:
The model variance increases during training, which shows a kind of overfitting. Now, I tried many ways to fix ...
0
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0answers
36 views
LightGBM boosting and bagging parameters
When training a gradient boosted decision tree model, I can use the LightGBM package to efficiently train my model. It's possible to define the hyperparameter search space with eg.
...
1
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0answers
40 views
Which Neural Network or Gradient Boosting framework is the simplest for Custom Loss Functions?
I need to implement a custom loss function.
The function is relatively simple:
$$-\sum \limits_{i=1}^m [O_{1,i} \cdot y_i-1] \ \cdot \ \operatorname{ReLu}(O_{1,i} \cdot \hat{y_i} - 1)$$
With $O$ being ...
0
votes
1answer
26 views
Scikit-learn estimator not changing predictions when random_state variable changes
I am trying to compute prediction intervals for a classifier I trained in scikit-learn. Even after setting a new random_state parameter in my pipeline, this does ...
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0answers
14 views
LGBM permutation invariance
I'm trying to train a binary classifier with LGBM over pairs of points in $\mathbb{R}^d$ (i.e. a classifier that takes a pair in $\mathbb{R}^{2d}$ and returns a class probability for the pair). I ...
0
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1answer
111 views
Why does Light GBM model produce different results while testing?
Using the Light GBM regressor, I have trained my data and, using Grid Search, I got the best parameters, but while testing with the best parameters I am getting different results each time, which ...
1
vote
1answer
100 views
Why is HistGradientBoostingRegressor in sklearn so fast and low on memory?
I trained multiple models for my problem and most ensemble algorithms resulted in lengthy fit and train time and huge model size on disk (approx 10GB for RandomForest) but when I tried ...
0
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0answers
18 views
Calibration of a few binary classifiers is not perfect - why?
I am working on a binary classifier using LightGBM. I try to see the results of the classifiers when changing the costs of false positives and false negatives, still working on the same training and ...
0
votes
1answer
24 views
Random Forest but keep only leaves with impurities below a threshold
Is there an algorithm out there that creates a random forest but then prunes all the leaves that have an impurity measure above a certain threshold that I would determine?
In other words, if I set min ...
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0answers
100 views
LightGBM model improvement when the focus is on probability prediction
I am building a binary classifier using LightGBM. The goal is not to predict the outcome as such, but rather to predict the probability of the target even. To be more specific, it's more about ranking ...
0
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0answers
56 views
LightGBM get model decision(rules)
I need to interpret the model decision for binary classification. Here is my model:
...
5
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2answers
152 views
What is the best way (cheapest / fastest option) to train an model on massive dataset (400GB+, 100m rows x 200 columns)?
I have a 400GB data set that I want to train a model on. What is the cheapest method to train this model? The options I can think of so far are:
AWS instance with massive RAM and train CPU (slow, but ...
0
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0answers
57 views
Tree Based Classification (XGBoost, LightGBM, etc) - Features from embeddings for sparse features?
I'm wondering if there is a possibility from using embeddings as inputs for tree based classification models?
For example we have a field called type of food, and ...
1
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0answers
34 views
cost sensitive loss function in lightbm with individual cost
i am looking for a cost sensitive function that will have weights according to individual row feature (like amount)
this way i can penalize more FN which has large amount vs. low dollar amount.
took ...
0
votes
1answer
98 views
Model Dump Parser (like XGBFI) for LightGBM and CatBoost
Currently my employer has multiple GLM in a live environment. I am interested in identifying new features and interactions to enhance the accuracy of these GLM; for now I am limited to the GLM ...
0
votes
1answer
45 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 ...
0
votes
1answer
95 views
Hyperparameter tuning with Bayesian-Optimization
I'm using LightGBM for the regression problem and here is my code.
...
1
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1answer
43 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
vote
1answer
162 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:
<...
1
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0answers
79 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
vote
1answer
138 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
211 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 ?
1
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2answers
1k 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
303 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
65 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 ...
2
votes
1answer
2k 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 ...
1
vote
0answers
71 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 ...
11
votes
2answers
3k 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
votes
1answer
34 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
votes
1answer
3k 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
votes
1answer
2k 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 ...
1
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0answers
214 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 ...
4
votes
1answer
2k 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 ...
1
vote
1answer
81 views
0
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1answer
1k 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
vote
1answer
163 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
1k 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:
...
