Mindsdb, In-Database Machine Learning, Hidden Markov Model For Time Series Processing, Output A Label As Such For Each Element In The Time Series

In this article, the author compares MindsDB and Pomegranate. MindsDB is a platform that specializes in machine learning using time series data processing with hidden Markov models. On the other hand, Pomegranate is a Python library that provides probability models like mixture and hidden Markov models for univariate and multivariate distributions.

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MindsDB

documentation

cloud mindsdb editor

warning: this thing could break your dependencies. better use docker instead.

docker pull mindsdb/mindsdb
# pip3 install mindsdb

HMMLearn (unsupervised)

most useful feature:

training and inferring the hidden states

supervised hmm learning

seqlearn

pomegranate (both supervised and unsupervised)

documentation

All models that support labeled data support semi-supervised learning, including naive Bayes classifiers, general Bayes classifiers, and hidden Markov models.

While probability Distributions are frequently used as components of more complex models such as mixtures and hidden Markov models, they can also be used by themselves. Many data science tasks require fitting a distribution to data or generating samples under a distribution. pomegranate has a large library of both univariate and multivariate distributions which can be used with an intuitive interface.

General Mixture Models (GMMs) are an unsupervised probabilistic model composed of multiple distributions (commonly referred to as components) and corresponding weights. This allows you to model more complex distributions corresponding to a singular underlying phenomena. For a full tutorial on what a mixture model is and how to use them, see the above tutorial.

Hidden Markov Models

Bayes Classifiers and Naive Bayes

Markov Chains

Bayesian Networks

Markov Networks