Auto_ViML VS evalml

Compare Auto_ViML vs evalml and see what are their differences.

Auto_ViML

Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request. (by AutoViML)
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Auto_ViML evalml
2 2
490 712
- 1.0%
4.2 8.7
5 months ago 5 days ago
Python Python
Apache License 2.0 BSD 3-clause "New" or "Revised" License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Auto_ViML

Posts with mentions or reviews of Auto_ViML. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-05-19.

evalml

Posts with mentions or reviews of evalml. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-05-19.

What are some alternatives?

When comparing Auto_ViML and evalml you can also consider the following projects:

mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Sklearn-genetic-opt - ML hyperparameters tuning and features selection, using evolutionary algorithms.

AutoViz - Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

easyopt - zero-code hyperparameters optimization framework

Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.

Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Python-Schema-Matching - A python tool using XGboost and sentence-transformers to perform schema matching task on tables.

sapientml - Generative AutoML for Tabular Data

SAP-HANA-AutoML - Python Automated Machine Learning library for tabular data.

Auto_TS - Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ram Seshadri. Collaborators welcome.

powershap - A power-full Shapley feature selection method.