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We are working on new way for visual programming. We developed a desktop application called MLJAR Studio. It is a notebook-based development environment with interactive code recipes and a managed Python environment. All running locally on your machine. We are waiting for your feedback. The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. It is designed to save time for a data scientist. It abstracts the common way to preprocess the data, construct the machine learning models, and perform hyper-parameter tuning to find the best model. It is no black box, as you can see exactly how the ML pipeline is constructed (with a detailed Markdown report for each ML model).
Features
- It uses many algorithms: Baseline, Linear, Random Forest, Extra Trees, LightGBM, Xgboost, CatBoost, Neural Networks, and Nearest Neighbors
- It can compute Ensemble based on a greedy algorithm from Caruana paper
- It can stack models to build a level 2 ensemble (available in Compete mode or after setting the stack_models parameter)
- It can do features preprocessing, like missing values imputation and converting categoricals. What is more, it can also handle target values preprocessing
- It can do advanced features engineering, like Golden Features, Features Selection, Text and Time Transformations
- It can tune hyper-parameters with a not-so-random-search algorithm (random-search over a defined set of values) and hill climbing to fine-tune final models
- It can compute the Baseline for your data so that you will know if you need Machine Learning or not
Project Samples
Categories
Machine LearningLicense
MIT LicenseFollow MLJAR Studio
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