TSCV VS Hyperactive

Compare TSCV vs Hyperactive and see what are their differences.

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TSCV Hyperactive
1 8
246 490
- -
0.0 7.7
over 1 year ago 5 months ago
Python Python
BSD 3-clause "New" or "Revised" License MIT 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.

TSCV

Posts with mentions or reviews of TSCV. We have used some of these posts to build our list of alternatives and similar projects.
  • [P] First release candidate of tscv v0.0.5
    1 project | /r/MachineLearning | 19 Mar 2021
    The wheel binary can be downloaded from my GitHub repo for early testing. If you notice any bug, please open a ticket in the repo. The final version is expected to be released by the end of this month, and users will be able to pip install it.

Hyperactive

Posts with mentions or reviews of Hyperactive. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-03-12.

What are some alternatives?

When comparing TSCV and Hyperactive you can also consider the following projects:

vectorbt - Find your trading edge, using the fastest engine for backtesting, algorithmic trading, and research.

mango - Parallel Hyperparameter Tuning in Python

autogluon - Fast and Accurate ML in 3 Lines of Code

pybobyqa - Python-based Derivative-Free Optimization with Bound Constraints

timebasedcv - Time based splits for cross validation

opytimizer - 🐦 Opytimizer is a Python library consisting of meta-heuristic optimization algorithms.

OpenMetadata - Open Standard for Metadata. A Single place to Discover, Collaborate and Get your data right.

optuna-examples - Examples for https://github.com/optuna/optuna

optimization-tutorial - Tutorials for the optimization techniques used in Gradient-Free-Optimizers and Hyperactive.

anovos - Anovos - An Open Source Library for Scalable feature engineering Using Apache-Spark

Auto_ViML - Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

Gradient-Free-Optimizers - Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.