pycm VS pyod

Compare pycm vs pyod and see what are their differences.

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pycm pyod
18 7
1,429 7,928
- -
5.0 7.7
2 months ago 21 days ago
Python Python
MIT License BSD 2-clause "Simplified" 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.

pyod

Posts with mentions or reviews of pyod. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-09-13.

What are some alternatives?

When comparing pycm and pyod you can also consider the following projects:

seq2seq - A general-purpose encoder-decoder framework for Tensorflow

tods - TODS: An Automated Time-series Outlier Detection System

spaCy - πŸ’« Industrial-strength Natural Language Processing (NLP) in Python

isolation-forest - A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.

AI-Expert-Roadmap - Roadmap to becoming an Artificial Intelligence Expert in 2022

alibi-detect - Algorithms for outlier, adversarial and drift detection

InvoiceNet - Deep neural network to extract intelligent information from invoice documents.

pycaret - An open-source, low-code machine learning library in Python

pretty-print-confusion-matrix - Confusion Matrix in Python: plot a pretty confusion matrix (like Matlab) in python using seaborn and matplotlib

stumpy - STUMPY is a powerful and scalable Python library for modern time series analysis

mlnotify - πŸ”” No need to keep checking your training - just one import line and you'll know the second it's done.

anomalib - An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.