Симулятор JWST на Python: как критика Хабра заставила выучить астрофизику и почему мы сделаем открытие раньше NASA...

#экзопланеты #JWST #машинное #обучение #астрофизика #python #XGBoost #NASA #биосигнатуры #открытая #наука

Origin | Interest | Match

Как мы сломали индекс обитаемости экзопланет: Парадокс ESI, Physics-Informed ML и 9600 фейковых «Земель» В прошлой нашей...

#экзопланеты #машинное #обучение #астрофизика #jwst #анализ #данных #xgboost #nasa #физика

Origin | Interest | Match

Космос из школьного кабинета: Как мы научили ИИ законам Кеплера после «разноса» от ученых Существует стере...

#экзопланеты #Астрофизика #машинное #обучение #Python #XGBoost #ExoLogica #AI #Kepler #NASA #KOI-4878

Origin | Interest | Match

Mastering CatBoost - Core Edition 📘 Mastering CatBoost: The Hidden Gem of Tabular AI (Early Access) A definitive, production-grade guide to CatBoost — one of the most powerful yet underused tools in modern machine learning.🛒 Pre-Order DetailsIncremental chapter releaseChapters are released progressively. As new material is added, both the value and the price increase over time.Lifetime updates includedBy pre-ordering, you receive all current and future updates for the book at no additional cost.By Valeriy Manokhin, PhD, MBA, CQF“CatBoost is not just underrated — it is objectively better.”This book explains why, with the science, benchmarks, and production-grade code to prove it.💸 PricingThis book is in active development and offered under early-access pricing. Early readers lock in lifetime access to all future updates The price will increase as content expands and the book approaches completion If you find value in the work — or want to support its continued development — you’re welcome to contribute what it’s worth to you ❤️🧠 Why CatBoost?There is now a substantial body of empirical evidence showing that CatBoost consistently and significantly outperforms XGBoost and LightGBM on real-world tabular data — often by large margins, as demonstrated in large-scale benchmarks such as TabArena.CatBoost is: faster at inference, easier to tune, and designed from the ground up for categorical features — without leakage-prone preprocessing hacks. Despite this, CatBoost remains one of the most underused tools in machine learning.This book fixes that.Built on: 🧪 Scientific benchmarks and peer-reviewed research 📈 Production experience and real-world pipelines 🔬 Direct links to the open-source ecosystem (including 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 label encoding or one-hot hacks Overfitting detection — native, automated safeguards Evaluation strategies — cross-validation the CatBoost way Interpretability — SHAP, feature importance, monotonic constraints Bonus: Time series with CatBoost, plus quantile & uncertainty modeling 📘 Scope & Depth — More Than “Just Boosting”Mastering CatBoost covers far more than standard classification use cases: Classification, regression, ranking, time series, and quantile / uncertainty models Deep treatment of categorical feature handling — CatBoost’s core advantage Native overfitting detection, monotonic constraints, and interpretability tools Everything tuned specifically for real-world tabular workflows 🏗️ Under-the-Hood Architecture & Scientific AdvantagesMany resources provide intuition and tuning advice.This book goes deeper.You’ll understand: Ordered boosting, symmetric trees, and smoothed target statistics Why CatBoost handles categorical variables without information leakage Why scientific benchmarks repeatedly show CatBoost outperforming XGBoost and LightGBM on real datasets Modern capabilities such as GPU training, quantization, and ONNX export 🧩 Interpretability & Safeguards Native overfitting detection — no guesswork Built-in feature importance, interactions, and partial dependence Monotonic constraints tuned specifically for CatBoost internals 🎯 The VerdictMastering CatBoost goes far beyond alternatives in: Technical depth (architecture + categorical handling) Applied scope (classification, regression, ranking, forecasting) Deployment readiness (quantization, ONNX, production pipelines) Support materials (repositories, notebooks, domain-specific chapters) 👨‍💻 Who This Book Is ForThis book is designed for: Machine learning engineers working with tabular data Data scientists tired of fragile pipelines and endless tuning Students or researchers who have hit limits with XGBoost or sklearn Practitioners who want to move fast from data to insight If you value speed, robustness, and real performance, this book is for you.📦 What You Get📥 Instant access — start reading immediately🔄 Free lifetime updates — new chapters, fixes, and bonus content💬 Private Discord access — discussions, materials, early bonuses, live events🔓 Pro Edition Bonus Pack (Early Access) Includes everything above, plus: ✅ Premium templates — plug-and-play workflows ✅ Extended case studies — deep analyses across major industries ✅ Cheat sheets & flashcards — quick-reference guides ✅ Behind-the-scenes notebooks — annotated exploratory pipelines ✅ Tabular model selection toolkit — benchmarking and optimisation notebooks Designed for professionals and teams who want to deploy faster and with confidence.⚠️ Pro Edition pricing will increase as bonus content expands and the book reaches completion.✍️ About the AuthorWritten by Valeriy Manokhin, PhD, MBA, CQF — a forecasting expert, data scientist, and machine learning researcher with publications in top peer-reviewed journals.Valeriy has advised startups and large enterprises, helping them build and rebuild forecasting and ML systems at scale. He has led successful initiatives for global organisations, winning competitive tenders against multinational consultancies and specialised AI vendors.His methods have delivered multimillion-dollar business impact, and his training programs have reached professionals in 40+ countries.His books are now used in 100+ countries and have ranked #1 in Machine Learning, Forecasting, and Time Series across major platforms.🌍 Trusted By and Taught ToProfessionals at:Amazon, Apple, Google, Meta, Nike, BlackRock, Morgan Stanley, Target, NTT Data, Mars Inc., Lidl, Publicis Sapient, and more.Researchers and academics 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 ForecastingA production-grade guide trusted by data science leaders in 100+ countries.Learn more → Mastering Modern Time Series Forecasting⚡ Ready to master the strongest tabular model in machine learning?CatBoost isn’t just another gradient booster.It’s one of the most underappreciated breakthroughs in ML — and you’re about to master it.👉 Get started now and build faster, stronger tabular models with confidence.

Mastering CatBoost: valeman.gumroad.com/...
Mastering CatBoost Pro: valeman.gumroad.com/...

MachineLearning #CatBoost #XGBoost #LightGBM #Calibration #DataScience #GradientBoosting

🔥 ML-guided design of flame-retardant polyurethane foams (Sun et al., 2026).

Using 800 #moleculardescriptors from #alvaDesc + formulation data, an #XGBoost model predicted LOI with R² = 0.84.

📖 Paper doi.org/10.1016/j.po...
🔗 alvaDesc: alvascience.com/alvadesc/

#cheminformatics #chemsky

Multidimensional contribution quantification strategy and XGBoost algorithm-based individual assessment method for cooperative learning For PBL practice courses teaching in college, a good collaborative learning assessment method is the key to effectively assess course outcomes. Using traditional assessment methods, for example, me...

New paper out!

Assessing individual contributions in PBL is tough—shared grades & peer review often lead to free riding and bias.

Read more: www.tandfonline.com/doi/full/10....

#ProjectBasedLearning #CooperativeLearning #LearningAnalytics #AIinEducation #XGBoost #HigherEd #IETI_Journal

Emulating the expansion of Antarctic perennial firn aquifers in the 21st century Abstract. Perennial firn aquifers (PFAs) are year-round bodies of liquid water within firns, which modulate meltwater runoff to crevasses, potentially impacting ice-shelf and ice-sheet stability. Rece...

New paper out in #TheCryosphere about #Antarctic perennial #firn aquifers in the 21st century.
By using a #XGBoost #emulator we use a ensemble of 12, with different scenarios, RCMs and GCMs.
tc.copernicus.org/articles/19/...

Insights into Air Quality Index (AQI) Variability with Explainable Machine Learning Techniques
www.mdpi.com/3042-5743/34...

By Claudio Andenna and Roberta Valentina Gagliardi
From the ECAS-7 Conference

#MachineLearning #XGBoost #SHAP #EnvironmentalScience

Dynamic Meta-Learning Boosts Adaptive XGBoost-Neural Ensemble

An ensemble combines XGBoost and neural networks via meta-learning, weighting with uncertainty and feature importance; benchmark results Sep 30 2025 show it outperforms both alone. Read more: getnews.me/dynamic-meta-learning-bo... #xgboost #metalearning

LSTM, Random Forest and XGBoost Compared for Solar & Wind Forecasting

A study compares LSTM, Random Forest and XGBoost for solar and wind forecasts, noting Random Forest offers faster inference while LSTM is the most complex model. Read more: getnews.me/lstm-random-forest-and-x... #lstm #randomforest #xgboost

LLMs vs Classical Machine Learning in COVID-19 Mortality Prediction

Using data from 9,134 COVID‑19 patients, XGBoost achieved an F1 of 0.87, while GPT‑4 in zero‑shot mode reached only 0.43; fine‑tuned Mistral‑7b improved to 0.74 F1. Read more: getnews.me/llms-vs-classical-machin... #covid19 #xgboost #llm

FOLD-SE Beats FOLD-R++ and XGBoost in Classification Performance

FOLD‑SE outperformed XGBoost on multi‑category classification, achieving higher precision with far lower computational effort, and produced a more compact rule set than FOLD‑R++ for binary tasks. getnews.me/fold-se-beats-fold-r-and... #foldse #xgboost

Stop Waiting: Make XGBoost 46x Faster With One Parameter Change Turn Hours of Training Into Minutes With GPU Acceleration If you’re a machine learning practitioner, you know this scene well. \ You’ve spent hours wrangling data, engineering the perfect features,...

Stop Waiting: Make XGBoost 46x Faster With One Parameter Change #Technology #SoftwareEngineering #ArtificialIntelligence #XGBoost #MachineLearning #DataScience

The HackerNoon Newsletter: Stop Waiting: Make XGBoost 46x Faster With One Parameter Change (9/13/2025) How are you, hacker? 🪐 What’s happening in tech today, September 13, 2025? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Osborne Computer Corp. Declares...

The HackerNoon Newsletter: Stop Waiting: Make XGBoost 46x Faster With One Parameter Change (9/13/2025) #Technology #SoftwareEngineering #ArtificialIntelligence #XGBoost #MachineLearning #PerformanceTuning

🚀 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

ObesityRiskPredictor - a Hugging Face Space by ethicalabs Classification with Random Forest, LightGBM and XGBoost.

"Obesity Risk Predictor" is tool designed to help identifying health risks based on lifestyle habits. The app lets you compare the performance of 3 different models (Random Forest, #LightGBM, and #XGBoost) on the same dataset

huggingface.co/spaces/ethic...

#MachineLearning #DataScience #MLSky

🚀 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

#xgboost

🚀 Pro Edition → Book + Bonus Pack "Mastering CatBoost: The Hidden Gem of Tabular AI " (Early Release Preorder) 🔥 Pro Edition: "Mastering CatBoost: The Hidden Gem of Tabular AI" (Early Access) The elite version of the book — trusted by data science leaders in 100+ countries.Unlock the premium toolkit behind today’s most powerful model for tabular data. 🚀🚀🚀 New: Pro Edition Now Available — $60 (USD) 🔥🔥🔥Includes everything in the standard edition plus: premium templates, cheat sheets, extended case studies, behind‑the‑scenes notebooks, model tuning toolkits, and access to live Q&A + AMA sessions with the author.⚠️ Final price of Pro Edition will rise to $150+ at book completion. See 📦 What You Get and 💸 Pricing for full details. 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. 🔓 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✅ 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. 💸 Pricing 🚀 Pro Edition Early Access: Price: $70 | Minimum: $60Includes the full book + Premium Pack.✅ Lock in now—price will rise to $150+ at full release. 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 ❤️. Ready to take your tabular machine learning skills to next level? 👉 Hit Buy Now, and if you want structured support, check out the course at 🧠 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.

Mastering CatBoost Pro -> valeman.gumroad.com/...

valeman.gumroad.com/...

#tabulardata #catboost #ai #xgboost

📢Simulation of lake underwater terrain based on the XGBoost model: a case study of typical lakes on the #TibetanPlateau
👉https://doi.org/10.1080/20964471.2025.2515713
#lake #underwater #terrain #XGBoost #topography #DEM #waterstorage #bathymetry #climatechange #hydrology #3D #remotesensing #GIS

🚀 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

🚀 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

n independent testing by AutoML framework mljar, CatBoost trounces XGBoost across all tasks from binary to multiclass classification to regression.

mljar.com/machine-le...

#CatBoost #xgboost

Unlike CatBoost and LightGBM, XGBoost could not even handle categorical features properly.

And then it just copied the idea from CatBoost and XGBoost.

When something is a copycat it is a laggard and follower, not innovator.

#CatBoost #xgboost

Meanwhile, CatBoost was built by a handful of people at Yandex and just… works.

#xgboost #catboost

🚀 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