PyCaret vs Weka
PyCaret
pycaret.orgWeka
weka.waikato.ac.nzSide by side
| Figure | PyCaret | Weka |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | Yes | Yes |
| Web | Yes | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 3 |
| Spec score · Predictive Analytics Software | 8.4 (#5) | 8.2 (#10) |
| Classification | ClassificationExperiment handles categorical targets and includes metrics such as accuracy, AUC, recall, precision, F1, kappa, and MCC. | Weka includes algorithms for assigning records to classes. |
| Clustering | ClusteringExperiment offers KMeans, affinity propagation, mean shift, spectral, hierarchical, DBSCAN, OPTICS, Birch, KMode, and HDBSCAN adapters. | The workbench supports unsupervised clustering analysis. |
| Compatibility limit | PyCaret 4.0 removes the 3.x module-level functional API, so the FAQ advises pinning 3.x for existing code and using 4.0 for new work. | Serialized Weka models created in version 3.7 are incompatible with version 3.8, and the documented migrator has at least one exception: RandomForest. |
| Documentation depth | The official site includes concepts, tutorials, preprocessing references, function guides, FAQs, a cheat sheet, and generated API reference. | Documentation includes a built-in manual, free book appendix, Javadocs, package documentation, videos, slides and a long-running mailing-list archive. |
| Headquarters | Toronto, Canada | Hamilton, New Zealand |
| Licence | PyCaret 4.0 licenses the engine under MIT and the control-plane components under BUSL-1.1 with a later Apache-2.0 change date and an additional-use grant. | Weka is GPL software; Weka 3.6 uses GPLv2 while versions later than 3.7.5 use GPLv3. |
| Purpose | PyCaret is an open-source, low-code Python tool for automating machine-learning workflows. | Weka is open-source machine learning software issued under the GNU General Public License. |
| Rating on G2 | 0.0 out of 5 from 0 ratings; the G2 category page provides no user-review information for PyCaret. | 4.3 out of 5 from 13 reviews |
| Regression | RegressionExperiment handles continuous targets and provides linear, tree, boosting, neural-network, and kernel models. | Weka includes regression methods for predicting numeric outcomes. |
| Support channels | The FAQ directs users to GitHub Discussions for questions, GitHub Issues for bugs, and LinkedIn for announcements. | The project directs users to mailing-list archives, help forums, bug reporting and an IRC channel. |
| What it does | PyCaret is a low-code Python AutoML library and self-hosted machine-learning platform. | Weka is a Java machine-learning workbench for data preparation, classification, regression, clustering, association-rule mining and visualization. |
Plans
In their own words
Weka
Weka is open-source machine-learning software for building and evaluating models, with support for forecasting and time-series modeling. It is available free under the GNU General Public License, with Weka 3.8 as the stable release and 3.9 as the development version. The stable branch receives fixes and feature updates intended to preserve compatibility; development builds may introduce breaking changes. Weka provides a Java API, Javadoc for its API and command-line parameters, and can also be used through Jupyter notebooks. Official releases require Java 8 or later. Downloads are listed for Windows, Mac OS, and Linux, with Intel and ARM options. Both branches include a package manager for community extensions, though installing packages requires internet access. The software includes built-in help and a comprehensive manual, with additional assistance through documentation, mailing list archives, forums, and a mailing list. Distributed derivative works must use the GPL; commercial projects needing Weka code in a non-GPL program may be able to obtain a suitable license. Models serialized in version 3.7 are incompatible with 3.8, and the migrator has an exception for RandomForest.