Advanced machine learning approaches for predicting compressive and flexural strength of carbon nanotube–reinforced cement composites

Introducing a robust #MachineLearning framework utilizing #GradientBoosting to accurately predict the strength of CNT-reinforced cement, featuring SHAP-based interpretability and a user interface for optimized mix design.
Nanotechnology Reviews #OpenAccess: doi.org/10.1515/ntre...

Statistical Inference Advances for Gradient Boosting Regression

Research adds a statistical inference add‑on to gradient boosting, offering built‑in confidence intervals and hypothesis tests. Submitted Sep 2025. Read more: getnews.me/statistical-inference-ad... #gradientboosting #machinelearning

Introducing Fréchet Geodesic Boosting for Non‑Euclidean Machine Learning

FGBoost extends gradient boosting to non‑Euclidean outputs using geodesic residuals and the Fréchet mean, outperforming boosting on three tasks, as detailed in a 23‑page paper. getnews.me/introducing-frechet-geod... #fgboost #gradientboosting

https://machinelearningmastery.com/how-to-decide-between-random-forests-and-gradient-boosting/

Top algorithms for structured data in machine learning: #RandomForests and #GradientBoosting. Enhance data analysis and prediction accuracy. #MachineLearning #DataScience machinelearningmastery.com/how-to-decide-between-ra...

🚀 Mastering CatBoost: The Hidden Gem of Tabular AI (Early Release Preorder) 🛒 Pre‑Order Details Incremental chapter release: Chapters are released gradually. As content drops, both the value and price climb over time. Lifetime updates included: By preordering now, you’ll receive every current and future update for the book at no extra cost. By Valeriy Manokhin, PhD, MBA, CQF “CatBoost is not just underrated—it’s objectively better.”This book shows you why, with the science and the code to prove it. 💸 Pricing 🎉 Launch Price: $30 | Minimum: $25Will increase to $60+ as content grows. As the content continues to grow, if you find value in it—or simply want to support the project—you're welcome to contribute whatever it’s worth to you ❤️. 🧠 Why CatBoost?There’s a preponderance of scientific evidence that CatBoost consistently and significantly (20%+ according to TabArena outperforms XGBoost, and LightGBM on real-world tabular data.It's faster in inference, easier to tune, and built from the ground up for categorical features—without the usual preprocessing hacks.Despite this, CatBoost remains one of the most underused tools in machine learning. This book fixes that.🧪 Backed by research, benchmarks, and production experience📈 Practical, readable, hands-on for working data scientists🔬 Linked to the open-source repo: Awesome CatBoost🔍 What You’ll Learn Core architecture: how CatBoost works under the hood Hands-on modeling: end-to-end tabular ML pipelines Categorical encoding: no more label/one-hot hacks Overfitting detection: built-in, automated safeguards Evaluation strategies: cross-validation the CatBoost way Interpretability: SHAP, feature importance, monotonic constraints Bonus: Time series with CatBoost + quantile & uncertainty modeling 📘 Scope & Depth: More than Just Boosters Mastering CatBoost covers: Not just classification, but regression, ranking, time series, and even quantile/uncertainty models Deep dive into categorical feature handling (CatBoost’s core advantage) Native overfitting detection, monotonic constraints, and interpretability tools all built-in and tuned for tabular workflows 🏗️ Under-the-Hood Architecture & Scientific Advantages Harrison’s book provides intuition and tuning advice, with code examples and deployment workflows . Mastering CatBoost delves into: Ordered boosting, symmetric trees, and smoothed target statistics — explaining why CatBoost handles categorical variables without leakage Scientific benchmarks consistently show CatBoost outperforming XGBoost and LightGBM on real-world tabular datasets Includes newer capabilities like GPU optimizations, quantization, and ONNX export 🧩 Interpretability & Safeguards Native overfitting detection, eliminating guesswork Built-in per-feature importance, interaction, and partial dependence tools Monotonic constraints tuned specifically for CatBoost internals 🎯 The Verdict Mastering CatBoost goes far beyond: In technical depth (architecture + categorical handling) Applied scope (classification, regression, ranking, forecasting) Deployment readiness (quantization, ONNX, real-world pipelines) Support materials (Awesome_CatBoost repo, notebooks, domain-specific chapters) 👨‍💻 Who Is This For?This book is designed for: Machine learning engineers using tabular datasets Data scientists tired of endless hyperparameter tuning Students or researchers who’ve hit limits with XGBoost or sklearn Practitioners who want to move fast from data to insight If you like fast iteration, fewer bugs, and state-of-the-art tabular models, this book is for you.📦 What You Get📥 Instant access to the book — start reading immediately.🔄 Free updates — including new chapters, bug fixes, and bonus content.💬 Exclusive access to the private Discord community — connect with fellow readers, get additional materials, early bonuses, special discounts, and join live events with the author.🔓 Pro Edition Bonus Pack (Early Access – $60) 🔥🔥🔥 Includes everything above, plus:✅ Premium Templates — plug-and-play workflows✅ Extended Case Studies — deep analyses across major industries✅ Cheat Sheets & Flashcards — quick-reference model guides and best practices✅ Behind-the-Scenes Notebooks — annotated walkthroughs and exploratory pipelines✅ Tabular Model Selection Toolkit — Python notebooks to benchmark, optimize, and compare📈 Ideal for professionals and teams who want to build and deploy faster—and sidestep the guesswork.✍️ About the AuthorWritten by Valeriy Manokhin, PhD, MBA, CQF — a seasoned forecasting expert, data scientist, and machine learning researcher with publications in top academic journals.Valeriy has advised both startups and large enterprises, helping them build and rebuild forecasting systems at scale. He has led successful forecasting initiatives for global organizations — including winning competitive tenders from multinational companies, outperforming major consulting firms like BCG and specialized AI startups focused on forecasting. He has delivered production-grade solutions for industry leaders such as Stanley Black & Decker and GfK.His methods have driven multimillion-dollar business impact, and his training programs have reached professionals in over 40 countries. This book is now used in more than 100+ countries and has become a #1-ranked title in Machine Learning, Forecasting, and Time Series across major platforms.🌍 Trusted By and Taught ToValeriy’s expertise is trusted by leaders at:Amazon, Apple, Google, Meta, Nike, BlackRock, Morgan Stanley, Target, NTT Data, Mars Inc., Lidl, Publicis Sapient, and more.His frameworks are followed by professionals from:University of Chicago, KTH (Sweden), UBC (Canada), DTU (Denmark), and other world-class institutions.👤 Students include:VPs of Engineering, AI Leads, Principal & Lead Data Scientists, ML Engineers, Consultants, Professors, Founders, Researchers, and PhD students.📚 Also by the AuthorMastering Modern Time Series ForecastingThe book trusted by data science leaders in 100+ countries. Unlock the toolkit behind today’s most powerful forecasting systems.Learn more → MasteringModernTimeSeriesForecasting⚡ Ready to Master the Best Tabular Model in ML?CatBoost isn’t just another gradient booster.It’s the most underappreciated breakthrough in machine learning—and you’re about to master it.👉 Grab your copy now and start building faster, better models with less tuning.

If you work with tabular data, this is your unfair advantage. Preorder now.

valeman.gumroad.com/...

#MachineLearning #DataScience #CatBoost #XGBoost #Kaggle #GradientBoosting #AI #MLEngineering #MasteringCatBoost

Whether you’re aiming to win Kaggle competitions, deploy robust models in production, or simply level up your ML toolkit, Mastering CatBoost will get you there.
#MachineLearning #DataScience #CatBoost #ML #AI #GradientBoosting #GBDT #Kaggle #Python #MLOps #TabularData #BookLaunch

Whether you’re aiming to win Kaggle competitions, deploy robust models in production, or simply level up your ML toolkit, Mastering CatBoost will get you there.
#MachineLearning #DataScience #CatBoost #ML #AI #GradientBoosting #GBDT #Kaggle #Python #MLOps #TabularData #BookLaunch

Whether you’re aiming to win Kaggle competitions, deploy robust models in production, or simply level up your ML toolkit, Mastering CatBoost will get you there.
#MachineLearning #DataScience #CatBoost #ML #AI #GradientBoosting #GBDT #Kaggle #Python #MLOps #TabularData #BookLaunch

🚀 Mastering CatBoost: The Hidden Gem of Tabular AI (Early Release Preorder) 🛒 Pre‑Order Details Incremental chapter release: Chapters are released gradually. As content drops, both the value and price climb over time. Lifetime updates included: By preordering now, you’ll receive every current and future update for the book at no extra cost. By Valeriy Manokhin, PhD, MBA, CQF “CatBoost is not just underrated—it’s objectively better.”This book shows you why, with the science and the code to prove it. 💸 Pricing 🎉 Launch Price: $30 | Minimum: $25Will increase to $60+ as content grows. As the content continues to grow, if you find value in it—or simply want to support the project—you're welcome to contribute whatever it’s worth to you ❤️. 🧠 Why CatBoost?There’s a preponderance of scientific evidence that CatBoost consistently and significantly (20%+ according to TabArena outperforms XGBoost, and LightGBM on real-world tabular data.It's faster in inference, easier to tune, and built from the ground up for categorical features—without the usual preprocessing hacks.Despite this, CatBoost remains one of the most underused tools in machine learning. This book fixes that.🧪 Backed by research, benchmarks, and production experience📈 Practical, readable, hands-on for working data scientists🔬 Linked to the open-source repo: Awesome CatBoost🔍 What You’ll Learn Core architecture: how CatBoost works under the hood Hands-on modeling: end-to-end tabular ML pipelines Categorical encoding: no more label/one-hot hacks Overfitting detection: built-in, automated safeguards Evaluation strategies: cross-validation the CatBoost way Interpretability: SHAP, feature importance, monotonic constraints Bonus: Time series with CatBoost + quantile & uncertainty modeling 📘 Scope & Depth: More than Just Boosters Mastering CatBoost covers: Not just classification, but regression, ranking, time series, and even quantile/uncertainty models Deep dive into categorical feature handling (CatBoost’s core advantage) Native overfitting detection, monotonic constraints, and interpretability tools all built-in and tuned for tabular workflows 🏗️ Under-the-Hood Architecture & Scientific Advantages Harrison’s book provides intuition and tuning advice, with code examples and deployment workflows . Mastering CatBoost delves into: Ordered boosting, symmetric trees, and smoothed target statistics — explaining why CatBoost handles categorical variables without leakage Scientific benchmarks consistently show CatBoost outperforming XGBoost and LightGBM on real-world tabular datasets Includes newer capabilities like GPU optimizations, quantization, and ONNX export 🧩 Interpretability & Safeguards Native overfitting detection, eliminating guesswork Built-in per-feature importance, interaction, and partial dependence tools Monotonic constraints tuned specifically for CatBoost internals 🎯 The Verdict Mastering CatBoost goes far beyond: In technical depth (architecture + categorical handling) Applied scope (classification, regression, ranking, forecasting) Deployment readiness (quantization, ONNX, real-world pipelines) Support materials (Awesome_CatBoost repo, notebooks, domain-specific chapters) 👨‍💻 Who Is This For?This book is designed for: Machine learning engineers using tabular datasets Data scientists tired of endless hyperparameter tuning Students or researchers who’ve hit limits with XGBoost or sklearn Practitioners who want to move fast from data to insight If you like fast iteration, fewer bugs, and state-of-the-art tabular models, this book is for you.📦 What You Get📥 Instant access to the book — start reading immediately.🔄 Free updates — including new chapters, bug fixes, and bonus content.💬 Exclusive access to the private Discord community — connect with fellow readers, get additional materials, early bonuses, special discounts, and join live events with the author.🔓 Pro Edition Bonus Pack (Early Access – $60) 🔥🔥🔥 Includes everything above, plus:✅ Premium Templates — plug-and-play workflows✅ Extended Case Studies — deep analyses across major industries✅ Cheat Sheets & Flashcards — quick-reference model guides and best practices✅ Behind-the-Scenes Notebooks — annotated walkthroughs and exploratory pipelines✅ Tabular Model Selection Toolkit — Python notebooks to benchmark, optimize, and compare📈 Ideal for professionals and teams who want to build and deploy faster—and sidestep the guesswork.✍️ About the AuthorWritten by Valeriy Manokhin, PhD, MBA, CQF — a seasoned forecasting expert, data scientist, and machine learning researcher with publications in top academic journals.Valeriy has advised both startups and large enterprises, helping them build and rebuild forecasting systems at scale. He has led successful forecasting initiatives for global organizations — including winning competitive tenders from multinational companies, outperforming major consulting firms like BCG and specialized AI startups focused on forecasting. He has delivered production-grade solutions for industry leaders such as Stanley Black & Decker and GfK.His methods have driven multimillion-dollar business impact, and his training programs have reached professionals in over 40 countries. This book is now used in more than 100+ countries and has become a #1-ranked title in Machine Learning, Forecasting, and Time Series across major platforms.🌍 Trusted By and Taught ToValeriy’s expertise is trusted by leaders at:Amazon, Apple, Google, Meta, Nike, BlackRock, Morgan Stanley, Target, NTT Data, Mars Inc., Lidl, Publicis Sapient, and more.His frameworks are followed by professionals from:University of Chicago, KTH (Sweden), UBC (Canada), DTU (Denmark), and other world-class institutions.👤 Students include:VPs of Engineering, AI Leads, Principal & Lead Data Scientists, ML Engineers, Consultants, Professors, Founders, Researchers, and PhD students.📚 Also by the AuthorMastering Modern Time Series ForecastingThe book trusted by data science leaders in 100+ countries. Unlock the toolkit behind today’s most powerful forecasting systems.Learn more → MasteringModernTimeSeriesForecasting⚡ Ready to Master the Best Tabular Model in ML?CatBoost isn’t just another gradient booster.It’s the most underappreciated breakthrough in machine learning—and you’re about to master it.👉 Grab your copy now and start building faster, better models with less tuning.

If you work with tabular data, this is your unfair advantage. Preorder now.

valeman.gumroad.com/...

#MachineLearning #DataScience #CatBoost #XGBoost #Kaggle #GradientBoosting #AI #MLEngineering #MasteringCatBoost

Whether you’re aiming to win Kaggle competitions, deploy robust models in production, or simply level up your ML toolkit, Mastering CatBoost will get you there.
#MachineLearning #DataScience #CatBoost #ML #AI #GradientBoosting #GBDT #Kaggle #Python #MLOps #TabularData #BookLaunch

🚀 Mastering CatBoost: The Hidden Gem of Tabular AI (Early Release Preorder) 🛒 Pre‑Order Details Incremental chapter release: Chapters are released gradually. As content drops, both the value and price climb over time. Lifetime updates included: By preordering now, you’ll receive every current and future update for the book at no extra cost. By Valeriy Manokhin, PhD, MBA, CQF “CatBoost is not just underrated—it’s objectively better.”This book shows you why, with the science and the code to prove it. 💸 Pricing 🎉 Launch Price: $30 | Minimum: $25Will increase to $60+ as content grows. As the content continues to grow, if you find value in it—or simply want to support the project—you're welcome to contribute whatever it’s worth to you ❤️. 🧠 Why CatBoost?There’s a preponderance of scientific evidence that CatBoost consistently and significantly (20%+ according to TabArena outperforms XGBoost, and LightGBM on real-world tabular data.It's faster in inference, easier to tune, and built from the ground up for categorical features—without the usual preprocessing hacks.Despite this, CatBoost remains one of the most underused tools in machine learning. This book fixes that.🧪 Backed by research, benchmarks, and production experience📈 Practical, readable, hands-on for working data scientists🔬 Linked to the open-source repo: Awesome CatBoost🔍 What You’ll Learn Core architecture: how CatBoost works under the hood Hands-on modeling: end-to-end tabular ML pipelines Categorical encoding: no more label/one-hot hacks Overfitting detection: built-in, automated safeguards Evaluation strategies: cross-validation the CatBoost way Interpretability: SHAP, feature importance, monotonic constraints Bonus: Time series with CatBoost + quantile & uncertainty modeling 📘 Scope & Depth: More than Just Boosters Mastering CatBoost covers: Not just classification, but regression, ranking, time series, and even quantile/uncertainty models Deep dive into categorical feature handling (CatBoost’s core advantage) Native overfitting detection, monotonic constraints, and interpretability tools all built-in and tuned for tabular workflows 🏗️ Under-the-Hood Architecture & Scientific Advantages Harrison’s book provides intuition and tuning advice, with code examples and deployment workflows . Mastering CatBoost delves into: Ordered boosting, symmetric trees, and smoothed target statistics — explaining why CatBoost handles categorical variables without leakage Scientific benchmarks consistently show CatBoost outperforming XGBoost and LightGBM on real-world tabular datasets Includes newer capabilities like GPU optimizations, quantization, and ONNX export 🧩 Interpretability & Safeguards Native overfitting detection, eliminating guesswork Built-in per-feature importance, interaction, and partial dependence tools Monotonic constraints tuned specifically for CatBoost internals 🎯 The Verdict Mastering CatBoost goes far beyond: In technical depth (architecture + categorical handling) Applied scope (classification, regression, ranking, forecasting) Deployment readiness (quantization, ONNX, real-world pipelines) Support materials (Awesome_CatBoost repo, notebooks, domain-specific chapters) 👨‍💻 Who Is This For?This book is designed for: Machine learning engineers using tabular datasets Data scientists tired of endless hyperparameter tuning Students or researchers who’ve hit limits with XGBoost or sklearn Practitioners who want to move fast from data to insight If you like fast iteration, fewer bugs, and state-of-the-art tabular models, this book is for you.📦 What You Get📥 Instant access to the book — start reading immediately.🔄 Free updates — including new chapters, bug fixes, and bonus content.💬 Exclusive access to the private Discord community — connect with fellow readers, get additional materials, early bonuses, special discounts, and join live events with the author.🔓 Pro Edition Bonus Pack (Early Access – $60) 🔥🔥🔥 Includes everything above, plus:✅ Premium Templates — plug-and-play workflows✅ Extended Case Studies — deep analyses across major industries✅ Cheat Sheets & Flashcards — quick-reference model guides and best practices✅ Behind-the-Scenes Notebooks — annotated walkthroughs and exploratory pipelines✅ Tabular Model Selection Toolkit — Python notebooks to benchmark, optimize, and compare📈 Ideal for professionals and teams who want to build and deploy faster—and sidestep the guesswork.✍️ About the AuthorWritten by Valeriy Manokhin, PhD, MBA, CQF — a seasoned forecasting expert, data scientist, and machine learning researcher with publications in top academic journals.Valeriy has advised both startups and large enterprises, helping them build and rebuild forecasting systems at scale. He has led successful forecasting initiatives for global organizations — including winning competitive tenders from multinational companies, outperforming major consulting firms like BCG and specialized AI startups focused on forecasting. He has delivered production-grade solutions for industry leaders such as Stanley Black & Decker and GfK.His methods have driven multimillion-dollar business impact, and his training programs have reached professionals in over 40 countries. This book is now used in more than 100+ countries and has become a #1-ranked title in Machine Learning, Forecasting, and Time Series across major platforms.🌍 Trusted By and Taught ToValeriy’s expertise is trusted by leaders at:Amazon, Apple, Google, Meta, Nike, BlackRock, Morgan Stanley, Target, NTT Data, Mars Inc., Lidl, Publicis Sapient, and more.His frameworks are followed by professionals from:University of Chicago, KTH (Sweden), UBC (Canada), DTU (Denmark), and other world-class institutions.👤 Students include:VPs of Engineering, AI Leads, Principal & Lead Data Scientists, ML Engineers, Consultants, Professors, Founders, Researchers, and PhD students.📚 Also by the AuthorMastering Modern Time Series ForecastingThe book trusted by data science leaders in 100+ countries. Unlock the toolkit behind today’s most powerful forecasting systems.Learn more → MasteringModernTimeSeriesForecasting⚡ Ready to Master the Best Tabular Model in ML?CatBoost isn’t just another gradient booster.It’s the most underappreciated breakthrough in machine learning—and you’re about to master it.👉 Grab your copy now and start building faster, better models with less tuning.

If you work with tabular data, this is your unfair advantage. Preorder now.

valeman.gumroad.com/...

#MachineLearning #DataScience #CatBoost #XGBoost #Kaggle #GradientBoosting #AI #MLEngineering #MasteringCatBoost

Whether you’re aiming to win Kaggle competitions, deploy robust models in production, or simply level up your ML toolkit, Mastering CatBoost will get you there.
#MachineLearning #DataScience #CatBoost #ML #AI #GradientBoosting #GBDT #Kaggle #Python #MLOps #TabularData #BookLaunch

🚀 Mastering CatBoost: The Hidden Gem of Tabular AI (Early Release Preorder) 🛒 Pre‑Order Details Incremental chapter release: Chapters are released gradually. As content drops, both the value and price climb over time. Lifetime updates included: By preordering now, you’ll receive every current and future update for the book at no extra cost. By Valeriy Manokhin, PhD, MBA, CQF “CatBoost is not just underrated—it’s objectively better.”This book shows you why, with the science and the code to prove it. 💸 Pricing 🎉 Launch Price: $30 | Minimum: $25Will increase to $60+ as content grows. As the content continues to grow, if you find value in it—or simply want to support the project—you're welcome to contribute whatever it’s worth to you ❤️. 🧠 Why CatBoost?There’s a preponderance of scientific evidence that CatBoost consistently and significantly (20%+ according to TabArena outperforms XGBoost, and LightGBM on real-world tabular data.It's faster in inference, easier to tune, and built from the ground up for categorical features—without the usual preprocessing hacks.Despite this, CatBoost remains one of the most underused tools in machine learning. This book fixes that.🧪 Backed by research, benchmarks, and production experience📈 Practical, readable, hands-on for working data scientists🔬 Linked to the open-source repo: Awesome CatBoost🔍 What You’ll Learn Core architecture: how CatBoost works under the hood Hands-on modeling: end-to-end tabular ML pipelines Categorical encoding: no more label/one-hot hacks Overfitting detection: built-in, automated safeguards Evaluation strategies: cross-validation the CatBoost way Interpretability: SHAP, feature importance, monotonic constraints Bonus: Time series with CatBoost + quantile & uncertainty modeling 📘 Scope & Depth: More than Just Boosters Mastering CatBoost covers: Not just classification, but regression, ranking, time series, and even quantile/uncertainty models Deep dive into categorical feature handling (CatBoost’s core advantage) Native overfitting detection, monotonic constraints, and interpretability tools all built-in and tuned for tabular workflows 🏗️ Under-the-Hood Architecture & Scientific Advantages Harrison’s book provides intuition and tuning advice, with code examples and deployment workflows . Mastering CatBoost delves into: Ordered boosting, symmetric trees, and smoothed target statistics — explaining why CatBoost handles categorical variables without leakage Scientific benchmarks consistently show CatBoost outperforming XGBoost and LightGBM on real-world tabular datasets Includes newer capabilities like GPU optimizations, quantization, and ONNX export 🧩 Interpretability & Safeguards Native overfitting detection, eliminating guesswork Built-in per-feature importance, interaction, and partial dependence tools Monotonic constraints tuned specifically for CatBoost internals 🎯 The Verdict Mastering CatBoost goes far beyond: In technical depth (architecture + categorical handling) Applied scope (classification, regression, ranking, forecasting) Deployment readiness (quantization, ONNX, real-world pipelines) Support materials (Awesome_CatBoost repo, notebooks, domain-specific chapters) 👨‍💻 Who Is This For?This book is designed for: Machine learning engineers using tabular datasets Data scientists tired of endless hyperparameter tuning Students or researchers who’ve hit limits with XGBoost or sklearn Practitioners who want to move fast from data to insight If you like fast iteration, fewer bugs, and state-of-the-art tabular models, this book is for you.📦 What You Get📥 Instant access to the book — start reading immediately.🔄 Free updates — including new chapters, bug fixes, and bonus content.💬 Exclusive access to the private Discord community — connect with fellow readers, get additional materials, early bonuses, special discounts, and join live events with the author.🔓 Pro Edition Bonus Pack (Early Access – $60) 🔥🔥🔥 Includes everything above, plus:✅ Premium Templates — plug-and-play workflows✅ Extended Case Studies — deep analyses across major industries✅ Cheat Sheets & Flashcards — quick-reference model guides and best practices✅ Behind-the-Scenes Notebooks — annotated walkthroughs and exploratory pipelines✅ Tabular Model Selection Toolkit — Python notebooks to benchmark, optimize, and compare📈 Ideal for professionals and teams who want to build and deploy faster—and sidestep the guesswork.✍️ About the AuthorWritten by Valeriy Manokhin, PhD, MBA, CQF — a seasoned forecasting expert, data scientist, and machine learning researcher with publications in top academic journals.Valeriy has advised both startups and large enterprises, helping them build and rebuild forecasting systems at scale. He has led successful forecasting initiatives for global organizations — including winning competitive tenders from multinational companies, outperforming major consulting firms like BCG and specialized AI startups focused on forecasting. He has delivered production-grade solutions for industry leaders such as Stanley Black & Decker and GfK.His methods have driven multimillion-dollar business impact, and his training programs have reached professionals in over 40 countries. This book is now used in more than 100+ countries and has become a #1-ranked title in Machine Learning, Forecasting, and Time Series across major platforms.🌍 Trusted By and Taught ToValeriy’s expertise is trusted by leaders at:Amazon, Apple, Google, Meta, Nike, BlackRock, Morgan Stanley, Target, NTT Data, Mars Inc., Lidl, Publicis Sapient, and more.His frameworks are followed by professionals from:University of Chicago, KTH (Sweden), UBC (Canada), DTU (Denmark), and other world-class institutions.👤 Students include:VPs of Engineering, AI Leads, Principal & Lead Data Scientists, ML Engineers, Consultants, Professors, Founders, Researchers, and PhD students.📚 Also by the AuthorMastering Modern Time Series ForecastingThe book trusted by data science leaders in 100+ countries. Unlock the toolkit behind today’s most powerful forecasting systems.Learn more → MasteringModernTimeSeriesForecasting⚡ Ready to Master the Best Tabular Model in ML?CatBoost isn’t just another gradient booster.It’s the most underappreciated breakthrough in machine learning—and you’re about to master it.👉 Grab your copy now and start building faster, better models with less tuning.

If you work with tabular data, this is your unfair advantage. Preorder now.

valeman.gumroad.com/...

#MachineLearning #DataScience #CatBoost #XGBoost #Kaggle #GradientBoosting #AI #MLEngineering #MasteringCatBoost

Whether you’re aiming to win Kaggle competitions, deploy robust models in production, or simply level up your ML toolkit, Mastering CatBoost will get you there.
#MachineLearning #DataScience #CatBoost #ML #AI #GradientBoosting #GBDT #Kaggle #Python #MLOps #TabularData #BookLaunch

🚀 Mastering CatBoost: The Hidden Gem of Tabular AI (Early Release Preorder) 🛒 Pre‑Order Details Incremental chapter release: Chapters are released gradually. As content drops, both the value and price climb over time. Lifetime updates included: By preordering now, you’ll receive every current and future update for the book at no extra cost. By Valeriy Manokhin, PhD, MBA, CQF “CatBoost is not just underrated—it’s objectively better.”This book shows you why, with the science and the code to prove it. 💸 Pricing 🎉 Launch Price: $30 | Minimum: $25Will increase to $60+ as content grows. As the content continues to grow, if you find value in it—or simply want to support the project—you're welcome to contribute whatever it’s worth to you ❤️. 🧠 Why CatBoost?There’s a preponderance of scientific evidence that CatBoost consistently and significantly (20%+ according to TabArena outperforms XGBoost, and LightGBM on real-world tabular data.It's faster in inference, easier to tune, and built from the ground up for categorical features—without the usual preprocessing hacks.Despite this, CatBoost remains one of the most underused tools in machine learning. This book fixes that.🧪 Backed by research, benchmarks, and production experience📈 Practical, readable, hands-on for working data scientists🔬 Linked to the open-source repo: Awesome CatBoost🔍 What You’ll Learn Core architecture: how CatBoost works under the hood Hands-on modeling: end-to-end tabular ML pipelines Categorical encoding: no more label/one-hot hacks Overfitting detection: built-in, automated safeguards Evaluation strategies: cross-validation the CatBoost way Interpretability: SHAP, feature importance, monotonic constraints Bonus: Time series with CatBoost + quantile & uncertainty modeling 📘 Scope & Depth: More than Just Boosters Mastering CatBoost covers: Not just classification, but regression, ranking, time series, and even quantile/uncertainty models Deep dive into categorical feature handling (CatBoost’s core advantage) Native overfitting detection, monotonic constraints, and interpretability tools all built-in and tuned for tabular workflows 🏗️ Under-the-Hood Architecture & Scientific Advantages Harrison’s book provides intuition and tuning advice, with code examples and deployment workflows . Mastering CatBoost delves into: Ordered boosting, symmetric trees, and smoothed target statistics — explaining why CatBoost handles categorical variables without leakage Scientific benchmarks consistently show CatBoost outperforming XGBoost and LightGBM on real-world tabular datasets Includes newer capabilities like GPU optimizations, quantization, and ONNX export 🧩 Interpretability & Safeguards Native overfitting detection, eliminating guesswork Built-in per-feature importance, interaction, and partial dependence tools Monotonic constraints tuned specifically for CatBoost internals 🎯 The Verdict Mastering CatBoost goes far beyond: In technical depth (architecture + categorical handling) Applied scope (classification, regression, ranking, forecasting) Deployment readiness (quantization, ONNX, real-world pipelines) Support materials (Awesome_CatBoost repo, notebooks, domain-specific chapters) 👨‍💻 Who Is This For?This book is designed for: Machine learning engineers using tabular datasets Data scientists tired of endless hyperparameter tuning Students or researchers who’ve hit limits with XGBoost or sklearn Practitioners who want to move fast from data to insight If you like fast iteration, fewer bugs, and state-of-the-art tabular models, this book is for you.📦 What You Get📥 Instant access to the book — start reading immediately.🔄 Free updates — including new chapters, bug fixes, and bonus content.💬 Exclusive access to the private Discord community — connect with fellow readers, get additional materials, early bonuses, special discounts, and join live events with the author.🔓 Pro Edition Bonus Pack (Early Access – $60) 🔥🔥🔥 Includes everything above, plus:✅ Premium Templates — plug-and-play workflows✅ Extended Case Studies — deep analyses across major industries✅ Cheat Sheets & Flashcards — quick-reference model guides and best practices✅ Behind-the-Scenes Notebooks — annotated walkthroughs and exploratory pipelines✅ Tabular Model Selection Toolkit — Python notebooks to benchmark, optimize, and compare📈 Ideal for professionals and teams who want to build and deploy faster—and sidestep the guesswork.✍️ About the AuthorWritten by Valeriy Manokhin, PhD, MBA, CQF — a seasoned forecasting expert, data scientist, and machine learning researcher with publications in top academic journals.Valeriy has advised both startups and large enterprises, helping them build and rebuild forecasting systems at scale. He has led successful forecasting initiatives for global organizations — including winning competitive tenders from multinational companies, outperforming major consulting firms like BCG and specialized AI startups focused on forecasting. He has delivered production-grade solutions for industry leaders such as Stanley Black & Decker and GfK.His methods have driven multimillion-dollar business impact, and his training programs have reached professionals in over 40 countries. This book is now used in more than 100+ countries and has become a #1-ranked title in Machine Learning, Forecasting, and Time Series across major platforms.🌍 Trusted By and Taught ToValeriy’s expertise is trusted by leaders at:Amazon, Apple, Google, Meta, Nike, BlackRock, Morgan Stanley, Target, NTT Data, Mars Inc., Lidl, Publicis Sapient, and more.His frameworks are followed by professionals from:University of Chicago, KTH (Sweden), UBC (Canada), DTU (Denmark), and other world-class institutions.👤 Students include:VPs of Engineering, AI Leads, Principal & Lead Data Scientists, ML Engineers, Consultants, Professors, Founders, Researchers, and PhD students.📚 Also by the AuthorMastering Modern Time Series ForecastingThe book trusted by data science leaders in 100+ countries. Unlock the toolkit behind today’s most powerful forecasting systems.Learn more → MasteringModernTimeSeriesForecasting⚡ Ready to Master the Best Tabular Model in ML?CatBoost isn’t just another gradient booster.It’s the most underappreciated breakthrough in machine learning—and you’re about to master it.👉 Grab your copy now and start building faster, better models with less tuning.

If you work with tabular data, this is your unfair advantage. Preorder now.

valeman.gumroad.com/...

#MachineLearning #DataScience #CatBoost #XGBoost #Kaggle #GradientBoosting #AI #MLEngineering #MasteringCatBoost

Whether you’re aiming to win Kaggle competitions, deploy robust models in production, or simply level up your ML toolkit, Mastering CatBoost will get you there.
#MachineLearning #DataScience #CatBoost #ML #AI #GradientBoosting #GBDT #Kaggle #Python #MLOps #TabularData #BookLaunch

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📊 Discover the power of Gradient Boosting in machine learning with Trevor Hastie's insightful lecture! 

Learn how boosting can enhance your models' performance. 
https://f.mtr.cool/mwtmhcizbz

#MachineLearning #DataScience #GradientBoosting #ml

✳️ #MachineLearning

🔹 Suele ofrecer mejores predicciones que la regresión
🔹 Técnicas como #RandomForest y #GradientBoosting captan las no linealidades y las interacciones entre elementos sin necesidad de tanto preprocesamiento

#BigData #CienciaDeDatos #predicciones #hoteles

GitHub - yubin-park/cs534: CS534 - Machine Learning in Fall 2020 at Emory University CS534 - Machine Learning in Fall 2020 at Emory University - yubin-park/cs534

#MachineLearning #HealthcareAI #DataScience #PaloBoost #GradientBoosting #AI #HealthTech

github.com/yubin-par...

How does it compare to XGBoost and LightGBM in your work? Drop a comment below!

#MachineLearning #DataScience #GradientBoosting #CatBoost #AI #XGBoost #LightGBM

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3. #Gradientboosting is VERY interpretable with the #SHAPley method. They are totally misleading by saying their Deep Neural Network is more interpretable and boosting is not interpretable. They are apparently ignorant of these important advanced in interpretability more than 5 years old now.

A Closer Look at Deep Learning on Tabular Data Tabular data is prevalent across various domains in machine learning. Although Deep Neural Network (DNN)-based methods have shown promising performance comparable to tree-based ones, in-depth...

For a deeper dive into the study, explore the full paper here -> arxiv.org/abs/2407.0...

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Isn’t it time to see what it can do for your projects?

#MachineLearning #DataScience #CatBoost #TabularData #AI #GradientBoosting #AwesomeRepo #DataAnalysis