Feature Engine provides Python tools for feature engineering and selection that are compatible with scikit-learn.
Open source
24 alternativesHow do mljar-supervised and lightly compare? Here are the facts side by side: pricing, platforms, open-source status and what each one is for.
| Directory detail | mljar-supervised | lightly |
|---|---|---|
| What it is | mljar-supervised provides tabular data AutoML with feature engineering, hyperparameter tuning, explanations, and documentation. | The lightly Python library supports self supervised learning on images. |
| Website | ||
| Type | Open-source project | Open-source project |
| Topic | ||
| Pricing | Not confirmed | Not confirmed |
| Platforms | Not recorded | Not recorded |
| Open source | Yes | Yes |
| Language | English | English |
| Alternatives |
Sites similar to both mljar-supervised and lightly.
Feature Engine provides Python tools for feature engineering and selection that are compatible with scikit-learn.
The Featuretools Python library supports automated feature engineering and is published as open source.
tsfresh automatically extracts relevant features from time series data.
Snorkel helps generate training data programmatically through weak supervision.
Little Ball of Fur provides graph sampling extensions for NetworKit and NetworkX.
pytorch tabular provides a unified framework for deep learning models on tabular data.