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Tuning the learning rate parameter for GBDT models

I've always been taught that decreasing the learning rate parameter in gbdt models such as XGBoost, LightGBM and Catboost will improve the out-of-sample performance, assuming the number of iterations ...
Casper's user avatar
  • 21
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0 answers
65 views

Custom loss function for multi label classification in catboost?

I have a data frame which I want to use for multi class classification problem. There are total 6 classes (say a, b, c, d, e, f). I want to improve the precision for three classes (say a, b, c) i.e. ...
SUNITA GUPTA's user avatar
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0 answers
22 views

How to control and optimize optuna

i'm here with a pretty open question. I'm using Optuna to fine tune a Catboost Regressor and i've found, trying a set of parameters by hand, that the "best params" it outputs are not the ...
Alessandro Togni's user avatar
0 votes
0 answers
47 views

Training Biased/Uneven Categorical Data with CatBoost, Unbalanced/Unseen Categories Handling

Summary: I am training a discount eligibility model where the dataset represents historical data for products where people availed discounts based on simple features like product category, discount ...
glory9211's user avatar
  • 101
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1 answer
858 views

How to do grid search for Catboost with categorical_cols

I know it's easy to do grid search for a simple Catboost model, such as in here: https://medium.com/aiplusoau/hyperparameter-tuning-a5fe69d2a6c7 by running something like ...
Ian's user avatar
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0 answers
87 views

Visualize Catboost and XGBoost training process + Cross Validation

I want to optimize Catboost and XGBoost models and visualize this process such that: Use 3-fold cross-validation Use my own pre-processing pipeline (Missing value imputation, over- or undersampling) ...
Ars ML's user avatar
  • 61
1 vote
2 answers
413 views

RandomizedSearchcv(n_iter=10) doesnt stop after training 10 models

I am using RandomizedSearchcv for hyperparameter optimization. When I run the model, it shows the scores for each model training. The problem is, it trains way more than 10 models when in fact I ...
Mehmet Deniz's user avatar
1 vote
0 answers
248 views

Optuina pruning during CrossValidation, does it make sense?

I'm currently trying to build a model using CatBoost. For my parameter tuning, I'm using optuna and cross-validation and pruning the trial checking on the intermediate cross-validation scores. Here ...
GiusWestsideDS's user avatar
0 votes
1 answer
329 views

SMOTENC oversampling without one-hot encoding

I'm using SMOTENC to oversample an imbalanced-dataset. I thought the point of SMOTENC was to give the option to oversample categorical features without one-hot encoding them. The reason I don't want ...
Boots's user avatar
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2 votes
0 answers
69 views

Catboost: Categorcial Feature Encoding

I would like to understand all the methods available in Catboost for encoding categorical features. Unfortunately, the published articles by Yandex ("CatBoost: gradient boosting with categorical ...
calpyte's user avatar
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1 vote
1 answer
138 views

If I use Weight of Evidence to transform categorical variables, do I still need to inform their indexes to Catboost

I'm using Weight of Evidence (WOE) to encode my categorical features. Do I still need to inform Catboost that they are categorical features by using cat_features parameter?
Jorge Amaral's user avatar
1 vote
0 answers
48 views

Intuition behind catboost encoding techniques

Can anyone please help me in understanding the effect of various bucketing techniques used in CatBoost Algorithm for categorical features? Like there is border, buckets, binarized target mean, counter ...
Mimansa Maheshwari's user avatar
1 vote
1 answer
426 views

Select threshold (cut-off point )for binary classification by desired fpr persentage value

I want to recreate catboost.utils.select_threshold(desc) method for CalibratedClassifierCV model. In Catboost I can select ...
Michael's user avatar
  • 131
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1 answer
111 views

CatBoost solves the problem of bias in pointwise gradient estimates

I've been reading the following papers: https://arxiv.org/abs/1810.11363, https://arxiv.org/abs/1706.09516 and https://www.researchgate.net/publication/318030603_Fighting_biases_with_dynamic_boosting. ...
Patricia Brezeanu's user avatar
2 votes
1 answer
555 views

How do we target-encode categorical features in multi class classification problems?

Say I have a multiclass problem with a dataset as this: ...
CutePoison's user avatar
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1 answer
970 views

How does Catboost regressor deal with categorical features at predict time?

I understand that Catboost regressor uses target-based encoding to convert categorical features to numerical features when training. But how does Catboost deal with categorical features at predict ...
cap99's user avatar
  • 3
1 vote
0 answers
110 views

Catboost not working properly when I remove non important variables (source of randomness?)

I was wondering if anyone has encountered the same. The thing is, when I run a catboost boosting model, delete non important variables (feature importance by prediction importance = 0, in fact these ...
Tom's user avatar
  • 65
2 votes
1 answer
4k views

How to tell CatBoost which feature is categorical?

I am excited to learn that CatBoost can handle categorical features by itself. One of my features, Department ID, is categorical. However, it looks like numeric, since the values are like 1001, 1002, ....
Fred Chang's user avatar
1 vote
0 answers
205 views

How to tune a Catboost Regressor

I have been trying to study about hyperparameter tuning for CatBoost regressor for my regression problem. The only issue being I can't figure out what all parameters should I tune for my use case out ...
MLlearner15's user avatar
4 votes
1 answer
976 views

Are linear models better when dealing with too many features? If so, why?

I had to build a classification model in order to predict which what would be the user rating by using his/her review. (I was dealing with this dataset: Trip Advisor Hotel Reviews) After some ...
dsbr__0's user avatar
  • 191
0 votes
1 answer
417 views

Catboost not able to handle a very simple dataset?

This is a post from a newbie and so might be a really poor question based on lack of knowledge. Thank you kindly! I'm using Catboost, which seems excellent, to fit a trivial dataset. The results are ...
user5406764's user avatar
1 vote
1 answer
155 views

Difference between model score on test part and Kaggle public score

I tested my CatBoostModel model on part of data and get 0.92 score, but Kaggle public score was 0.9. I found new hyperparameters via randomsearch, new model score was 0.925, but on Kaggle score fell ...
Dmitry  Sokolov's user avatar
1 vote
0 answers
2k views

How to use the eval set in catboost appropriately?

Let's say you have a dataset, and you split it into 80% training and 20% testing. Naturally, you want to find the optimal hyperparameters for your model, so with the training set, you plan to do cross ...
user125720's user avatar
1 vote
1 answer
622 views

Unable to tune hyperparameters for CatBoostRegressor

I am trying to fit a CatBoostRegressor to my model. When I perform K fold CV for the baseline model everything works fine. But when I use Optuna for hyperparameter tuning, it does something really ...
spectre's user avatar
  • 2,105
0 votes
1 answer
29 views

How do Classification Algorithms such as Catboost and Random Forest parse test data?

I would like to know how classification works with the algorithms listed above. My specific question is this, say I have a high signal continuous feature which has a certain distribution and I train a ...
Nathan's user avatar
  • 1
0 votes
1 answer
230 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 ...
bradS's user avatar
  • 1,575
0 votes
1 answer
260 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 ...
Aastha Jha's user avatar
1 vote
1 answer
271 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 ...
Chong Lip Phang's user avatar
1 vote
0 answers
190 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, ...
Mamoud's user avatar
  • 11
2 votes
1 answer
662 views

Catboost multiclassification evaluation metric: Kappa & WKappa

I am working on an unbalanced classification problem and i want to use Kappa as my evaluation metric. Considering the classifier accepts weights (which i have given it), should i still be using ...
Musa's user avatar
  • 31
0 votes
1 answer
537 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 ?
user702846's user avatar
0 votes
0 answers
194 views

How to understand the definition of Greedy Target-based Statistics in the CatBoost paper

There is a method named Target statistics to deal with categorical features in the catboost paper. I still some confusion about the mathematical form. Could you some guys to expain how to compute it! $...
tktktk0711's user avatar
3 votes
0 answers
84 views

What is the concept behind the categorical-encoding used in the CatBoost benchmark problems?

I'm working through CatBoost quality benchmark problems (here). I'm particularly intrigued by the methodology adopted to convert categorical features to numerical values as described in the ...
PPR's user avatar
  • 171
1 vote
2 answers
896 views

Does gradient boosting algorithm error always decrease faster and lower on training data?

I am building another XGBoost model and I'm really trying not to overfit the data. I split my data into train and test set and fit the model with early stopping based on the test-set error which ...
Xaume's user avatar
  • 192
6 votes
1 answer
757 views

How to achieve SHAP values for a CatBoost model in R?

I'm asked to create a SHAP analysis in R but I cannot find it how to obtain it for a CatBoost model. I can get the SHAP values of an XGBoost model with ...
user100740's user avatar
22 votes
1 answer
13k views

Lightgbm vs xgboost vs catboost

I've seen that in Kaggle competitions people are using lightgbms where they used to use xgboost. My question is: when would you rather use xgboost instead of lightgbm? What about catboost?
David Masip's user avatar
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