Beyond excited to share that catboost now has full native support in tidymodels!!
This support should extend to all applicable areas of tidymodels, which means that orbital works as well 🚀
opensource.posit.co/blog/2026-06...
#rstats #tidymodels
This support should extend to all applicable areas of tidymodels, which means that orbital works as well 🚀
opensource.posit.co/blog/2026-06...
#rstats #tidymodels
CatBoost support in tidymodels
CatBoost is now available as a supported engine in tidymodels. This post shows how to train, tune, and use CatBoost models through the familiar parsnip interface.
opensource.posit.co
June 25, 2026 at 5:28 PM
Beyond excited to share that catboost now has full native support in tidymodels!!
This support should extend to all applicable areas of tidymodels, which means that orbital works as well 🚀
opensource.posit.co/blog/2026-06...
#rstats #tidymodels
This support should extend to all applicable areas of tidymodels, which means that orbital works as well 🚀
opensource.posit.co/blog/2026-06...
#rstats #tidymodels
XGBoost? Dinosaur tech. Struggles with categorical data like it's 2015. Still living off its early hype.
🚀 CatBoost dominates — fast, smart, and built to handle cats like a boss.
⚡ WarpGBM just dropped — think CatBoost, but turbocharged.
🚀 CatBoost dominates — fast, smart, and built to handle cats like a boss.
⚡ WarpGBM just dropped — think CatBoost, but turbocharged.
April 17, 2025 at 6:37 PM
XGBoost? Dinosaur tech. Struggles with categorical data like it's 2015. Still living off its early hype.
🚀 CatBoost dominates — fast, smart, and built to handle cats like a boss.
⚡ WarpGBM just dropped — think CatBoost, but turbocharged.
🚀 CatBoost dominates — fast, smart, and built to handle cats like a boss.
⚡ WarpGBM just dropped — think CatBoost, but turbocharged.
Disentangling sampling bias to generate early-warning surfaces of forest ecological sensitivity: An integrated MaxEnt–CatBoost framework www.sciencedirect.com/science/arti...
July 28, 2026 at 10:20 AM
Disentangling sampling bias to generate early-warning surfaces of forest ecological sensitivity: An integrated MaxEnt–CatBoost framework www.sciencedirect.com/science/arti...
Our latest blog post shows we can deploy boosted tree models, such as xgboost, lightgbm, or catboost, directly into the database itself!
Running predictionsfrom an xgboost model on 2.1 million observations in less than 10 seconds!
posit.co/blog/deployi...
#rstats #tidymodels #mlops
Running predictionsfrom an xgboost model on 2.1 million observations in less than 10 seconds!
posit.co/blog/deployi...
#rstats #tidymodels #mlops
Deploying boosted tree models with Orbital - Posit
Orbital 0.5.0 translates tidymodels workflows into SQL, featuring new tuning tools to balance predictive accuracy with database performance.
posit.co
March 16, 2026 at 6:35 PM
Our latest blog post shows we can deploy boosted tree models, such as xgboost, lightgbm, or catboost, directly into the database itself!
Running predictionsfrom an xgboost model on 2.1 million observations in less than 10 seconds!
posit.co/blog/deployi...
#rstats #tidymodels #mlops
Running predictionsfrom an xgboost model on 2.1 million observations in less than 10 seconds!
posit.co/blog/deployi...
#rstats #tidymodels #mlops
возможно, это как-то связано с тем, что модель называется CatBoost
April 25, 2026 at 10:34 AM
возможно, это как-то связано с тем, что модель называется CatBoost
✅ CatBoost dominates single-model comparisons both out of the box and under tuning, beating XGBoost, LightGBM, deep nets, and hybrids.
🚨 What This Means for You
✔ Need reliable performance without extreme compute? CatBoost is your best bet—consistently outperforming XGBoost and LightGBM.
🚨 What This Means for You
✔ Need reliable performance without extreme compute? CatBoost is your best bet—consistently outperforming XGBoost and LightGBM.
July 6, 2025 at 4:06 PM
✅ CatBoost dominates single-model comparisons both out of the box and under tuning, beating XGBoost, LightGBM, deep nets, and hybrids.
🚨 What This Means for You
✔ Need reliable performance without extreme compute? CatBoost is your best bet—consistently outperforming XGBoost and LightGBM.
🚨 What This Means for You
✔ Need reliable performance without extreme compute? CatBoost is your best bet—consistently outperforming XGBoost and LightGBM.
February 22, 2025 at 5:20 PM
lol my first thought was catboost is the gayest 🏳️🌈
June 2, 2025 at 1:41 PM
lol my first thought was catboost is the gayest 🏳️🌈
orbital 0.5.0 is here! Deploy tidymodels #RStats workflows directly in databases. 🛰️
New: LightGBM, CatBoost, rpart support. Huge performance improvements via nested `case_when()` and `separate_trees` for ensemble models. Score **millions** of records in seconds,
tidyverse.org/blog/2026/03...
New: LightGBM, CatBoost, rpart support. Huge performance improvements via nested `case_when()` and `separate_trees` for ensemble models. Score **millions** of records in seconds,
tidyverse.org/blog/2026/03...
April 1, 2026 at 2:26 PM
orbital 0.5.0 is here! Deploy tidymodels #RStats workflows directly in databases. 🛰️
New: LightGBM, CatBoost, rpart support. Huge performance improvements via nested `case_when()` and `separate_trees` for ensemble models. Score **millions** of records in seconds,
tidyverse.org/blog/2026/03...
New: LightGBM, CatBoost, rpart support. Huge performance improvements via nested `case_when()` and `separate_trees` for ensemble models. Score **millions** of records in seconds,
tidyverse.org/blog/2026/03...
Plot twist: We should be using CatBoost
May 21, 2026 at 8:19 PM
Plot twist: We should be using CatBoost
catboost is incredible. almost no point in even tuning it
September 1, 2025 at 12:33 AM
catboost is incredible. almost no point in even tuning it
synthesizing realistic structured enterprise data is a challenge too, if this model could do that it could be a boon for hard-mining or uncertainty-sampling training data for the GOFAI catboost models we all use in production
September 16, 2026 at 3:45 PM
synthesizing realistic structured enterprise data is a challenge too, if this model could do that it could be a boon for hard-mining or uncertainty-sampling training data for the GOFAI catboost models we all use in production
Can routine blood tests accurately stage gastric cancer progression before it is too late? A new CatBoost machine learning model achieved an external validation AUC of up to 0.97 using just 27 routine lab features.
AI predicts stomach cancer using routine blood tests
A new machine learning model spots the slow march toward gastric cancer using basic lab work, but its struggle with middle-stage disease shows where clinical AI still needs to mature.
yesilscience.com
September 26, 2026 at 5:01 PM
Can routine blood tests accurately stage gastric cancer progression before it is too late? A new CatBoost machine learning model achieved an external validation AUC of up to 0.97 using just 27 routine lab features.
Amazon’s AI knows that CatBoost is all you need for machine learning on tabular data — and it’s putting serious real money behind that call.
For a limited time, the book is heavily subsidized: 40% off.
www.amazon.com/Maste...
For a limited time, the book is heavily subsidized: 40% off.
www.amazon.com/Maste...
September 24, 2026 at 4:30 PM
Amazon’s AI knows that CatBoost is all you need for machine learning on tabular data — and it’s putting serious real money behind that call.
For a limited time, the book is heavily subsidized: 40% off.
www.amazon.com/Maste...
For a limited time, the book is heavily subsidized: 40% off.
www.amazon.com/Maste...
🚀 **CatBoost Leads in Tabular Data Modeling!** 🚀
A comprehensive study analyzing 300 diverse datasets has reaffirmed CatBoost's dominance in tabular data tasks.
A comprehensive study analyzing 300 diverse datasets has reaffirmed CatBoost's dominance in tabular data tasks.
December 28, 2024 at 6:13 PM
🚀 **CatBoost Leads in Tabular Data Modeling!** 🚀
A comprehensive study analyzing 300 diverse datasets has reaffirmed CatBoost's dominance in tabular data tasks.
A comprehensive study analyzing 300 diverse datasets has reaffirmed CatBoost's dominance in tabular data tasks.
The damage on well-behaved models was not subtle.
— CatBoost: log-loss worsened on 93% of folds. Platt added 5.3%.
— TabICL: worse on 91% of folds. 6.0% penalty.
— EBM: worse on 90%. 4.4% penalty.
— TabPFN: worse on 87%. 5.0% penalty.
Four of the five best classifiers in the study.
— CatBoost: log-loss worsened on 93% of folds. Platt added 5.3%.
— TabICL: worse on 91% of folds. 6.0% penalty.
— EBM: worse on 90%. 4.4% penalty.
— TabPFN: worse on 87%. 5.0% penalty.
Four of the five best classifiers in the study.
September 26, 2026 at 4:30 PM
The damage on well-behaved models was not subtle.
— CatBoost: log-loss worsened on 93% of folds. Platt added 5.3%.
— TabICL: worse on 91% of folds. 6.0% penalty.
— EBM: worse on 90%. 4.4% penalty.
— TabPFN: worse on 87%. 5.0% penalty.
Four of the five best classifiers in the study.
— CatBoost: log-loss worsened on 93% of folds. Platt added 5.3%.
— TabICL: worse on 91% of folds. 6.0% penalty.
— EBM: worse on 90%. 4.4% penalty.
— TabPFN: worse on 87%. 5.0% penalty.
Four of the five best classifiers in the study.
We heard from the community that CatBoost is the way do go. We listened and learned! here is the first batch of updated hexes for #tidymodels
April 1, 2025 at 4:49 PM
We heard from the community that CatBoost is the way do go. We listened and learned! here is the first batch of updated hexes for #tidymodels
(2/2) Here is a tutorial by Joaquín Amat Rodrigo and Javier Escobar Ortiz for time series forecasting with the skforecast using XGBoost, LightGBM, Scikit-learn, and CatBoost models 🚀.
📖🔗: cienciadedatos.net/documentos/p...
📖🔗: cienciadedatos.net/documentos/p...
Forecasting time series with gradient boosting: Skforecast, XGBoost, LightGBM and CatBoost
Forecasting with gradient boosting models using python libraries xgboost, lightgbm, scikitlearn and catboost.
cienciadedatos.net
April 20, 2024 at 12:58 PM
(2/2) Here is a tutorial by Joaquín Amat Rodrigo and Javier Escobar Ortiz for time series forecasting with the skforecast using XGBoost, LightGBM, Scikit-learn, and CatBoost models 🚀.
📖🔗: cienciadedatos.net/documentos/p...
📖🔗: cienciadedatos.net/documentos/p...
{mlr3} and {mlr3extralearners} to run HPO on XGBoost and CatBoost with cuda enabled.
May 8, 2026 at 12:29 PM
{mlr3} and {mlr3extralearners} to run HPO on XGBoost and CatBoost with cuda enabled.
#Rstats
So today I learned about XGBoost and Catboost and I'm both frightened and intrigued! I am definitely on team "don't use ML when a linear model with seasonality will do" but these seem super interesting.
So today I learned about XGBoost and Catboost and I'm both frightened and intrigued! I am definitely on team "don't use ML when a linear model with seasonality will do" but these seem super interesting.
May 22, 2026 at 1:41 AM
#Rstats
So today I learned about XGBoost and Catboost and I'm both frightened and intrigued! I am definitely on team "don't use ML when a linear model with seasonality will do" but these seem super interesting.
So today I learned about XGBoost and Catboost and I'm both frightened and intrigued! I am definitely on team "don't use ML when a linear model with seasonality will do" but these seem super interesting.
Spatiotemporal prediction of forest litterfall in China by using multi-source data and Transformer-CatBoost model | Journal of Forestry Research link.springer.com/article/10.1...
Spatiotemporal prediction of forest litterfall in China by using multi-source data and Transformer-CatBoost model - Journal of Forestry Research
Forest litterfall is a key contributor to soil carbon accumulation. However, existing studies have primarily foused on site-level observations or annual-scale assessments, while the intra-annual dynam...
link.springer.com
December 18, 2025 at 1:07 PM
Spatiotemporal prediction of forest litterfall in China by using multi-source data and Transformer-CatBoost model | Journal of Forestry Research link.springer.com/article/10.1...
"We improved the F1 score by staggering 45% using CatBoost!"
"Great! Do we already know which campaign we should send?"
1/n 👇🏼
#causality #causalAI #machinelearning #CausalSky
"Great! Do we already know which campaign we should send?"
1/n 👇🏼
#causality #causalAI #machinelearning #CausalSky
December 15, 2023 at 7:07 PM
"We improved the F1 score by staggering 45% using CatBoost!"
"Great! Do we already know which campaign we should send?"
1/n 👇🏼
#causality #causalAI #machinelearning #CausalSky
"Great! Do we already know which campaign we should send?"
1/n 👇🏼
#causality #causalAI #machinelearning #CausalSky
The lab has started using CatBoost to predict selective constraint in mammalian coding sequences, and it is shocking how good it is. It could make it possible to test adaptation accounting for constraint as McDonald-Kreitman does, but without having to do a lot of resequencing to get PN/PS.
May 22, 2025 at 10:49 PM
The lab has started using CatBoost to predict selective constraint in mammalian coding sequences, and it is shocking how good it is. It could make it possible to test adaptation accounting for constraint as McDonald-Kreitman does, but without having to do a lot of resequencing to get PN/PS.
My recipe for time series forecasting with XGBoost, CatBoost or LightGBM (based on my recent experiments):
1. Pick between MLForecast or AutoGluonTS
2. Ask Sonnet 3.5 to convert one of the example notebooks to your dataset
3. Iterate on feature engineering/hyperparameters
1. Pick between MLForecast or AutoGluonTS
2. Ask Sonnet 3.5 to convert one of the example notebooks to your dataset
3. Iterate on feature engineering/hyperparameters
November 27, 2024 at 12:30 PM
My recipe for time series forecasting with XGBoost, CatBoost or LightGBM (based on my recent experiments):
1. Pick between MLForecast or AutoGluonTS
2. Ask Sonnet 3.5 to convert one of the example notebooks to your dataset
3. Iterate on feature engineering/hyperparameters
1. Pick between MLForecast or AutoGluonTS
2. Ask Sonnet 3.5 to convert one of the example notebooks to your dataset
3. Iterate on feature engineering/hyperparameters
New study in Journal of Molecular Liquids: ML predicts NMR chemical shifts for metal complexes (45Sc, 49Ti, 89Y, 91Zr, 139La). CatBoost+RDKit ≈7% RMSE (Sc/Y/La); SHAP highlights key features. Read: doi.org/10.1016/j.mo... #NMR #MachineLearning #TransitionMetals
August 29, 2025 at 11:07 AM
New study in Journal of Molecular Liquids: ML predicts NMR chemical shifts for metal complexes (45Sc, 49Ti, 89Y, 91Zr, 139La). CatBoost+RDKit ≈7% RMSE (Sc/Y/La); SHAP highlights key features. Read: doi.org/10.1016/j.mo... #NMR #MachineLearning #TransitionMetals