XGBoost
Hacker ApprovedRanked 8 of 19 machine learning libraries by recommenders
Recommended as a strong, fast baseline for tabular data that often beats neural networks. Main caveat: it needs a lot of feature engineering.
Recommended as a strong, fast baseline for tabular data that often beats neural networks. Main caveat: it needs a lot of feature engineering.
13 people gave an opinion
- recommend it
- 13
- criticize it
- 0
- approval share
- 100%
Recommended in 7 of 13 months
- November 2025: recommended
- December 2025: recommended
- January 2026: recommended
- February 2026: recommended
- March 2026: not recommended
- April 2026: not recommended
- May 2026: not recommended
- June 2026: not recommended
- July 2026: not recommended
- August 2026: recommended
- September 2026: recommended
- October 2026: recommended
- November 2026: not recommended (month in progress)
Why it carries the badge
- 13 people recommend itNeeds 10 or more, met.
- Nobody criticizes itNeeds 2 to 1 or better, met.
- Recommended in 7 of 13 monthsNeeds 4 or more, met.
The same three rules apply to every product, and the badge can’t be bought. How the badge works
What people say about it
The reasons people give, with the number of comments that give each one. Open a reason to read what they said.
Why people recommend it
Each mark is one comment
Beats neural networks on tabular data
“I have never seen tabular transformer models beat xgboost/catboost in industrial context where datasets is gigantic.”
Comment from 28 Sep 2026 in “TabPFN and TabICL vs. tuned XGBoost: the model that doesn't train won 14/14” “xgboost and lightgbm still outperformed anything NN based for tabular data”
Comment from 4 Aug 2026 in “Why Large Language Models Fail at Tabular Prediction” “reach for XGBoost/LightGBM for tabular data is also good advice”
Comment from 16 Feb 2026 in “Visual Introduction to PyTorch” - Comment from 23 Dec 2025 in “The Illustrated Transformer”
Strong results on varied prediction tasks
“I’ve gotten some really impressive results by just throwing xgboost on 25 classification related questions.”
Comment from 10 Oct 2026 in “Typesafe AI raises $870M at $7.5B” “TF-IDF with 1-gram features paired with XGBoost consistently emerged as the winner.”
Comment from 14 Nov 2025 in “Honda: 2 years of ml vs 1 month of prompting - heres what we learned” “xgboost model per timeseries has never been beaten before”
Comment from 19 Oct 2026 in “Timestral: Mistral's time-series foundation models” - Comment from 25 Oct 2026 in “A 10-year-old CNN outperforms frontier LLMs for skin cancer diagnosis”
Other machine learning libraries people recommend
The four most recommended other machine learning libraries, with the same counts for each, so you can compare like for like. The tick on each bar marks the 2 to 1 line the badge needs.
See all 19 machine learning libraries, rankedWhat people said
12 of the 13 comments that mention it, newest first. Each links to the original comment.
“training an xgboost or a deep learning model with a classification head is the way to go”
“Xgboost outperforms llms in algorithmic trading”
“xgboost model per timeseries has never been beaten before”
“I’ve gotten some really impressive results by just throwing xgboost on 25 classification related questions.”
“I have never seen tabular transformer models beat xgboost/catboost in industrial context where datasets is gigantic.”
“xgboost and lightgbm still outperformed anything NN based for tabular data”
“reach for XGBoost/LightGBM for tabular data is also good advice”
“a recommender system that would normally need FAISS + XGBoost + SHAP + Isolation Forests + custom code”
“you are still better off using boosted decision trees”
“XGBoost has the fastest inference time on GPUs”
“TF-IDF with 1-gram features paired with XGBoost consistently emerged as the winner.”
“good baseline, but the challenge is always that you need to spend a lot of time feature engineering”
Counts come from 1,795,640 Hacker News comments, November 2025 to November 2026. How we count. Report a mistake.