# shankarpandala/lazypredict

Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning

Repository: https://github.com/shankarpandala/lazypredict
Canonical: https://ross.abutalabs.com/products/lazypredict
Language: Python
License: MIT
License Family: permissive
Topics: machine-learning, automl, regression, classification
Last push: 2026-04-26T03:34:19+00:00

## Health v2 (maintenance only)
Score: 78/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 79, release rhythm 63, longevity 100
- inputs: {"age_days": 2482, "days_push": 129, "days_rel": 171, "gap_med": 77.0, "n_releases_24m": 5}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3347, forks 365 (observed 2026-08-28T04:07:57.654883+00:00)

## What it is
Lazy Predict is a Python library that trains dozens of machine learning models with minimal code to quickly identify which algorithms perform best on a dataset, without any parameter tuning. It covers classification, regression, and time series forecasting, with optional GPU acceleration and MLflow experiment tracking.

## Use cases
- quickly compare dozens of ML models on my dataset
- find the best classifier without tuning hyperparameters
- baseline all regression models with a few lines of code
- benchmark time series forecasting models automatically
- screen which algorithms are worth tuning for my problem
- run automated model selection for classification and regression

## When to choose
- you need fast baselines across many models before investing in tuning
- you want a low-code way to screen classifiers, regressors, or forecasting models
- you want built-in experiment tracking, GPU acceleration, or seasonal detection for time series

## When to avoid
- you need production-grade, fully tuned models rather than quick comparisons
- you require fine-grained control over each model's training pipeline
- your workflow depends on hyperparameter optimization, which Lazy Predict deliberately skips

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, data-science, benchmarking
- domain: machine-learning, data-science, time-series
- platform: python, cross-platform
- tags: automl, model-selection, scikit-learn, classification, regression, time-series-forecasting, model-comparison, low-code

## Member repositories
- shankarpandala/lazypredict (main) score 78

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:57.654883+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:41:00.144984+00:00, confidence not recorded.
  - readme: https://github.com/shankarpandala/lazypredict (fetched 2026-08-28T04:07:57.654883+00:00, sha e32a6459b678)
  - registry_pypi: https://pypi.org/pypi/lazypredict/json (fetched 2026-08-29T09:34:12.810718+00:00, sha 989492d506ca)
- Data as of 2026-08-30T08:39:29.467469+00:00.