Questions tagged [catboost]
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35 questions
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Does class weights guarantee calibration?
There is possibility to assign class weights while training classifiers, e.g. CatBoost. To the best of my knowledge it adds weight to objects in computation of loss function, therefore penalizing ...
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Does row order impact how a CatBoost algorithm is trained?
I'm dealing with an issue where I'm unable to replicate a particular result that I had previously obtained with CatBoost, using the same dataset (but where the rows are in different order).
First, I ...
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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 ...
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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. ...
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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
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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 ...
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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 ...
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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 ...
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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 ...
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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?
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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 ...
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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 ...
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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.
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How do we target-encode categorical features in multi class classification problems?
Say I have a multiclass problem with a dataset as this:
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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 ...
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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 ...
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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, ....
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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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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 ...
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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, ...
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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 ...
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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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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!
$...
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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 ...
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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 ...
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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
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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?
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