Automated Machine Learning with scikit-learn

Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ram Seshadri. Collaborators welcome.

autosklearn-zeroconf is a fully automated binary classifier. It is based on the AutoML challenge winner auto-sklearn. Give it a dataset with known outcomes (labels) and it returns a list of predicted outcomes for your new data. It even estimates the precision for you! The engine is tuning massively parallel ensemble of machine learning pipelines for best precision/recall.

Presentations on Auto-sklearn

auto-sklearn examples on Jupyter notebooks

In this repository we test AutoML approaches for time-series forecasting

Denoising Autoencoder wrapper (from Theano) to sklearn (scikit learn)

Benchmark for some usual automated machine learning, such as: AutoSklearn, MLJAR, H2O, TPOT and AutoGluon. All visualized via a Dash Web Application

TFG realizado en la Universidad de Burgos del desarrollo de una aplicación para el uso de un Radar de 60 GHz de la marca Acconeer.

Small tutorial on auto-sklearn which is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.

Warehouse Storage Optimization

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"Auto-sklearn for chemistry" - train and run machine-learned classifiers for molecular classification tasks.

This repository includes projects using datasets of structured data (non-Spark). The projects use Python, NumPy, Pandas, Matplotlib, Seaborn, TensorFlow, Pytorch, and Sklearn.

KGpip - A Scalable AutoML Approach Based on Graph Neural Networks

Explainable Automated Machine Learning Framework for Predicting the Risk of Major Adverse Cardiac Event (MACE)

This is a Python template for a machine learning project with tabular data which includes most common data manipulation steps as well as data profiling and visualiztion, dimension reduction and eventually modeling with hyperparameter tunning using sklearn pipeline. At the end we also use auto-sklearn package for an autoML process.

Auto-Sklearn tutorial for classification and regression

Unified Python library for XGBoost, LightGBM, and sklearn GBM with identical APIs. Features 9 optimization strategies, one-line auto-tuning, GPU acceleration, and comprehensive model analysis. Built with clean OOP architecture for easy algorithm extension.

AutoML（使用ライブラリー：Auto-sklearn, AutoGluon, AutoKeras, EvalML, FLAML, h2o. ludwig, mljar-supervised, PyCaret, TPOT）

MSFS-AutoSklearn file

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Openml compatibility wrappers for TPOT and Auto-Sklearn

Here I upload all automl like Tpot, Auto-Sklearn

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Auto ML, Auto Sklearn

Testing some Auto-ML libraries for fun

A stack for auto-sklearn

Example for using auto-sklearn