LightGBM
Shares rank 14 of 19 machine learning libraries by recommenders
Recommended as a default for tabular data, often beating neural networks, though most people note it still needs good feature engineering.
Recommended as a default for tabular data, often beating neural networks, though most people note it still needs good feature engineering.
7 people gave an opinion
- recommend it
- 7
- criticize it
- 0
- approval share
- 100%
Recommended in 5 of 13 months
- November 2025: not recommended
- December 2025: recommended
- January 2026: not recommended
- February 2026: recommended
- March 2026: 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: not recommended
- November 2026: not recommended (month in progress)
Why it doesn’t carry the badge
- 7 people recommend itNeeds 10 or more, not met.
- Nobody criticizes itNeeds 2 to 1 or better, met.
- Recommended in 5 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
Default choice for tabular data
“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” Works well with feature engineering
“performing miracles after a heck of a lot fearure engineering”
Comment from 28 Sep 2026 in “TabPFN and TabICL vs. tuned XGBoost: the model that doesn't train won 14/14” “You can use lightgbm with appropriate feature engineering.”
Comment from 31 Mar 2026 in “Google's 200M-parameter time-series foundation model with 16k context”
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
All 7 comments that mention it, newest first. Each links to the original comment.
“performing miracles after a heck of a lot fearure engineering”
“xgboost and lightgbm still outperformed anything NN based for tabular data”
“LightGBM won M5 and it wasn't even a competition.”
“You can use lightgbm with appropriate feature engineering.”
“inference is not usually slow for classical ML (save lightgbm)”
“reach for XGBoost/LightGBM for tabular data is also good advice”
“boosted decision trees (e.g. XGBoost, LightGBM, or CatBoost)”
Counts come from 1,795,640 Hacker News comments, November 2025 to November 2026. How we count. Report a mistake.