Practical overview
When it fits
Use Lazy Predict during an early modeling stage when you need a broad baseline comparison before committing effort to model-specific tuning.
Who it is for
- Python data scientists comparing candidate models for a new classification or regression task.
- Practitioners establishing time-series forecasting baselines across multiple model families.
- Developers who want a low-setup benchmark before selecting models for further tuning.
Documented uses
- Compare baseline classification models and inspect metrics and predictions.
- Benchmark regression candidates before choosing models for additional work.
- Compare forecasting approaches, including statistical, machine-learning and deep-learning models.
- Configure categorical encoding, cross-validation, timeouts or a custom evaluation metric.
- Track benchmark experiments with the built-in MLflow integration.
Requirements & limits
The public repository identifies the software as open source under the MIT license. The README lists pip and conda installation methods.
- It is a Python library integrated into a user's project, rather than a hosted benchmarking interface.
- The repository lists support for Python 3.9 through 3.13.
- Time-series, boosting, deep-learning and foundation-model options are installed through optional extras.
- GPU acceleration applies to supported models and is configured as an option; it is not required for the documented basic classification example.
- Target and binary categorical encoding require the separate
category-encodersdependency.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install Lazy Predict and prepare a classification benchmark
Set up the Python library and prepare train/test data for the documented classification workflow.
Before you start
- Python 3.9 through 3.13 is listed as supported.
- A Python environment with pip.
- A classification dataset with feature data and labels.
Steps
Install the package with the command below.
pip install lazypredictLoad or provide feature data
Xand target labelsy.Split the data into training and test sets using a method such as scikit-learn's
train_test_split.
Expected outcome
A Python environment with Lazy Predict installed and training/test data ready for the documented classifier fit stage.
Guide limitations
- These steps cover installation and data preparation, not a completed benchmark.
- Optional model families and GPU acceleration may require additional dependencies or supported hardware.
Run a classification comparison and review model results
Fit the documented LazyClassifier workflow and inspect its model comparison output.
Before you start
- Lazy Predict installed in a Python environment.
- Training and test feature sets and corresponding labels.
Steps
Import
LazyClassifierfromlazypredict.Supervised.Create a classifier, for example with
verbose=0,ignore_warnings=Trueandcustom_metric=None.Call
fit(X_train, X_test, y_train, y_test)and assign the returned models and predictions.Print or otherwise review the returned models table.
Expected outcome
A comparison of classification models and, in the documented example, a predictions result.
Guide limitations
- The README example demonstrates a specific classification workflow; it does not guarantee model performance on other datasets.
- The README describes benchmarking without parameter tuning, so further model selection and tuning remain separate work.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 10 October 2026
- Original website title
- shankarpandala/lazypredict · Official homepage title · retained in its source language