mljar-examples
machine_learning_basics
| mljar-examples | machine_learning_basics | |
|---|---|---|
| 2 | 5 | |
| 58 | 4,205 | |
| - | - | |
| 3.3 | 0.0 | |
| 5 months ago | 3 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
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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.
mljar-examples
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MLJAR Automated Machine Learning for Tabular Data (Stacking, Golden Features, Explanations, and AutoDoc)
All ML experiments have automatic documentation that creates Markdown reports ready to commit to the repo (example1, example2).
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Show HN: Mljar Automated Machine Learning for Tabular Data (Explanation,AutoDoc)
The creator here. I'm working on AutoML since 2016. I think that latest release (0.7.15) of MLJAR AutoML is amazing. It has ton of fantastic features that I always want to have in AutoML:
- Operates in three modes: Explain, Perform, Compete.
- `Explain` is for data exploratory and checking the default performance (without HP tuning). It has Automatic Exploratory Data Analysis.
- `Perform` is for building production-ready models (HP tuning + ensembling).
- `Compete` is for solving ML competitions in limited time amount (HP tuning + ensembling + stacking).
- All ML experiments have automatic documentation which creates Markdown reports ready to commit to the repo ([example](https://github.com/mljar/mljar-examples/tree/master/Income_c...)).
- The package produces extensive explanations: decision tree visualization, feature importance, SHAP explanations, advanced metrics values.
- It has advanced feature engineering, like: Golden Features, Features Selection, Time and Text Transformations, Categoricals handling with target, label, or one-hot encodings.
machine_learning_basics
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Bayesian linear regression in (plain) Python
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.
- Bayesian linear regression in Python
What are some alternatives?
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
Financial-Models-Numerical-Methods - Collection of notebooks about quantitative finance, with interactive python code.
igel - a delightful machine learning tool that allows you to train, test, and use models without writing code
100-Days-Of-ML-Code - 100 Days of ML Coding
automlbenchmark - OpenML AutoML Benchmarking Framework
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.
humble-benchmarks - Benchmarking programming languages using statistics and machine learning algorithms
mango - Parallel Hyperparameter Tuning in Python
trulens - Evaluation and Tracking for LLM Experiments
rmi - A learned index structure
PyImpetus - 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
Time-series-classification-and-clustering-with-Reservoir-Computing - Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.