All Projects → AmazaspShumik → Sklearn Bayes

AmazaspShumik / Sklearn Bayes

Licence: mit
Python package for Bayesian Machine Learning with scikit-learn API

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to Sklearn Bayes

Eli5
A library for debugging/inspecting machine learning classifiers and explaining their predictions
Stars: ✭ 2,477 (+478.74%)
Mutual labels:  jupyter-notebook, scikit-learn
Text Classification
Machine Learning and NLP: Text Classification using python, scikit-learn and NLTK
Stars: ✭ 239 (-44.16%)
Mutual labels:  jupyter-notebook, scikit-learn
Amazing Feature Engineering
Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
Stars: ✭ 218 (-49.07%)
Mutual labels:  jupyter-notebook, scikit-learn
Imodels
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Stars: ✭ 194 (-54.67%)
Mutual labels:  jupyter-notebook, scikit-learn
Scikit Learn Videos
Jupyter notebooks from the scikit-learn video series
Stars: ✭ 3,254 (+660.28%)
Mutual labels:  jupyter-notebook, scikit-learn
Explainx
Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.
Stars: ✭ 196 (-54.21%)
Mutual labels:  jupyter-notebook, scikit-learn
Kagglestruggle
Kaggle Struggle
Stars: ✭ 228 (-46.73%)
Mutual labels:  jupyter-notebook, scikit-learn
Virgilio
Virgilio is developed and maintained by these awesome people. You can email us virgilio.datascience (at) gmail.com or join the Discord chat.
Stars: ✭ 13,200 (+2984.11%)
Mutual labels:  jupyter-notebook, scikit-learn
Sklearn Evaluation
Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.
Stars: ✭ 294 (-31.31%)
Mutual labels:  jupyter-notebook, scikit-learn
Code
Compilation of R and Python programming codes on the Data Professor YouTube channel.
Stars: ✭ 287 (-32.94%)
Mutual labels:  jupyter-notebook, scikit-learn
Sklearn Benchmarks
A centralized repository to report scikit-learn model performance across a variety of parameter settings and data sets.
Stars: ✭ 194 (-54.67%)
Mutual labels:  jupyter-notebook, scikit-learn
Machine Learning Python
機器學習: Python
Stars: ✭ 316 (-26.17%)
Mutual labels:  jupyter-notebook, scikit-learn
Bet On Sibyl
Machine Learning Model for Sport Predictions (Football, Basketball, Baseball, Hockey, Soccer & Tennis)
Stars: ✭ 190 (-55.61%)
Mutual labels:  jupyter-notebook, scikit-learn
Data Science Projects With Python
A Case Study Approach to Successful Data Science Projects Using Python, Pandas, and Scikit-Learn
Stars: ✭ 198 (-53.74%)
Mutual labels:  jupyter-notebook, scikit-learn
Pqkmeans
Fast and memory-efficient clustering
Stars: ✭ 189 (-55.84%)
Mutual labels:  jupyter-notebook, scikit-learn
Deeplearning cv notes
📓 deepleaning and cv notes.
Stars: ✭ 223 (-47.9%)
Mutual labels:  jupyter-notebook, scikit-learn
Bert Sklearn
a sklearn wrapper for Google's BERT model
Stars: ✭ 182 (-57.48%)
Mutual labels:  jupyter-notebook, scikit-learn
Practical Machine Learning With Python
Machine Learning Tutorials in Python
Stars: ✭ 183 (-57.24%)
Mutual labels:  jupyter-notebook, scikit-learn
Machine Learning Notebooks
Stanford Machine Learning course exercises implemented with scikit-learn
Stars: ✭ 282 (-34.11%)
Mutual labels:  jupyter-notebook, scikit-learn
Zat
Zeek Analysis Tools (ZAT): Processing and analysis of Zeek network data with Pandas, scikit-learn, Kafka and Spark
Stars: ✭ 303 (-29.21%)
Mutual labels:  jupyter-notebook, scikit-learn

Python package for Bayesian Machine Learning with scikit-learn API

Build Status Coverage Status

alt text alt text alt text alt text

Installing & Upgrading package

pip install https://github.com/AmazaspShumik/sklearn_bayes/archive/master.zip
pip install --upgrade https://github.com/AmazaspShumik/sklearn_bayes/archive/master.zip

Algorithms

  • ARD Models
    • Relevance Vector Regression (version 2.0) code, tutorial
    • Relevance Vector Classifier (version 2.0) code, tutorial
    • Type II Maximum Likelihood ARD Linear Regression code
    • Type II Maximum Likelihood ARD Logistic Regression code, tutorial
    • Variational Relevance Vector Regression code
    • Variational Relevance Vector Classification code, tutorial
  • Decomposition Models
    • Restricted Boltzmann Machines (PCD-k / CD-k, weight decay, adaptive learning rate) code, tutorial
    • Latent Dirichlet Allocation (collapsed Gibbs Sampler) code, tutorial
  • Linear Models
    • Empirical Bayes Linear Regression code, tutorial
    • Empirical Bayes Logistic Regression (uses Laplace Approximation) code, tutorial
    • Variational Bayes Linear Regression code, tutorial
    • Variational Bayes Logististic Regression (uses Jordan local variational bound) code, tutorial
  • Mixture Models
    • Variational Bayes Gaussian Mixture Model with Automatic Model Selection code, tutorial
    • Variational Bayes Bernoulli Mixture Model code, tutorial
    • Dirichlet Process Bernoulli Mixture Model code
    • Dirichlet Process Poisson Mixture Model code
    • Variational Multinoulli Mixture Model code
  • Hidden Markov Models
    • Variational Bayes Poisson Hidden Markov Model code, demo
    • Variational Bayes Bernoulli Hidden Markov Model code
    • Variational Bayes Gaussian Hidden Markov Model code, demo

Contributions:

There are several ways to contribute (and all are welcomed)

 * improve quality of existing code (find bugs, suggest optimization, etc.)
 * implement machine learning algorithm (it should be bayesian; you should also provide examples & notebooks)
 * implement new ipython notebooks with examples 

Bitdeli Badge

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].