ML for Tabular Data in 2026: Why Trees Still Dominate Neural Networks

Описание: ​#MachineLearning #DataScience #TabularData #AI #GoogleCloud


Description:
Why do tree-based models still outperform Deep Learning for tabular data? In this video, we break down the current state of Machine Learning for tables, based on the latest 2026 benchmarks. We explore why MLPs often fail where Decision Forests thrive and look at the "new wave" of automated tools.

​What we cover:
​The "Tree Victory": Understanding why GBDT still wins in 2026.
​The New Wave: Deep dive into TabPFN (for small data) and TabSTAR.
​Production Powerhouses: Why AutoGluon is the go-to for many data scientists.
​Google's Ecosystem: Leveraging Vertex AI, AutoML Tables, and BigQuery ML for enterprise-scale data.

​The Decision Matrix: A simplified guide on which tool to use for your specific dataset.

​Timestamps:
0:00 The Tabular Data Challenge
1:20 Why Trees Beat Neural Networks (The MLP problem)
3:45 TabPFN: Revolution for Small Datasets
5:30 AutoGluon & TabSTAR: The Modern Stack
8:15 Google Cloud AI: BigQuery ML & Vertex AI
10:00 The Decision Matrix: How to choose your model?
12:30 Summary & Future Trends

​Copyright Disclaimer:
All trademarks, logos, and brand names (Google, Vertex AI, AutoGluon, etc.) are the property of their respective owners. This content is for educational purposes.