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? Mastering CatBoost: The Hidden Gem of Tabular AI (early access - release in 2025 / Early 2026)

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 - the book price will rise to $60+ as more chapters drop. Preorder now and lock in lifetime access.

As the content continue to grow, if you find value in it and want to support the project - you are welcome to contribute whatever it is woth to you ❤️.

? Why CatBoost?

There’s a preponderance of scientific evidence that CatBoost consistently and significantly (20%+ according to T abArena) outperforms XGBoost, 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 using Conformal Prediction

? 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 (one of CatBoost’s many advantages)
Native overfitting detection, monotonic constraints, and interpretability tools all built-in and tuned for tabular workflows

?️ Under-the-Hood Architecture & Scientific Advantages

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 published chapters 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.

✍️ About the Author

Written by Valeriy Manokhin, PhD, MBA, CQF — a seasoned AI, conformal prediction and 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 leading Fortune 500 companies/

His methods have driven multimillion-dollar business impact, and his training programs have reached professionals in over 40 countries. His book Mastering Modern Time Series Forecasting 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 To

Valeriy’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 Author

Mastering Modern Time Series Forecasting

The 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.

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