PyImpetus VS machine_learning_basics

Compare PyImpetus vs machine_learning_basics and see what are their differences.


PyImpetus is a Markov Blanket based feature subset selection algorithm that considers features both separately and together as a group in order to provide not just the best set of features but also the best combination of features (by atif-hassan)
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PyImpetus machine_learning_basics
1 5
111 4,104
- -
0.0 0.0
6 months ago 11 months ago
Jupyter Notebook Jupyter Notebook
MIT 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.
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Posts with mentions or reviews of PyImpetus. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning PyImpetus yet.
Tracking mentions began in Dec 2020.


Posts with mentions or reviews of machine_learning_basics. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-01-29.
  • Bayesian linear regression in (plain) Python
    2 projects | /r/Python | 29 Jan 2021
    A while back I open sourced a repository implementing fundamental machine learning algorithms in Python, along with the most important theoretical information. I originally created the repository for myself when preparing for AI residency interviews. You can find the original Reddit post here.
    2 projects | /r/Python | 29 Jan 2021

What are some alternatives?

When comparing PyImpetus and machine_learning_basics you can also consider the following projects:

Financial-Models-Numerical-Methods - Collection of notebooks about quantitative finance, with interactive python code.

100-Days-Of-ML-Code - 100 Days of ML Coding

homemade-machine-learning - 🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

mango - Parallel Hyperparameter Tuning in Python

borb-google-colab-examples - This repository contains some examples of using borb in google colab. These examples enable you to try out the features of borb without installing it on your system. They also ensure the system requirements and imports are all taken care of.

rmi - A learned index structure

iterative-grabcut - This algorithm uses a rectangle made by the user to identify the foreground item. Then, the user can edit to add or remove objects to the foreground. Then, it removes the background and makes it transparent.

trulens - Evaluation and Tracking for LLM Experiments

Time-series-classification-and-clustering-with-Reservoir-Computing - Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.

perceptron-asm - A single-layer perceptron in x86 assembly to distinguish between circles and rectangles.

hdbscan - A high performance implementation of HDBSCAN clustering.

Machine-Learning-Specialization-Coursera - Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and - Coursera (2022) by Prof. Andrew NG