Kaggle Kernels that use AutoML and Intel® Extension for Scikit-learn*

The following Kaggle kernels show how to patch autoML frameworks with Intel® Extension for Scikit-learn*.

TPS stands for Tabular Playground Series, which is a series of beginner-friendly Kaggle competitions.

Kernel	Goal
AutoML MultiClass Classification (Gradient Boosting, Random Forest, kNN) using AutoGluon with Intel® Extension for Scikit-learn* Data: [TPS Jun 2021] Synthetic eCommerce data	Predict the category of an eCommerce product
AutoML Binary Classification (Gradient Boosting, Random Forest, kNN) using AutoGluon with Intel® Extension for Scikit-learn* Data: Titanic datset	Predict whether a passenger survivies
AutoML Binary Classification (Gradient Boosting, Random Forest) using AutoGluon with Intel® Extension for Scikit-learn* Data: [TPS Oct 2021] Synthetic molecular response data	Predict the biological response of molecules given various chemical properties
AutoML Binary Classification (Gradient Boosting, Random Forest, kNN) using EvalML and AutoGluon with Intel® Extension for Scikit-learn* Data: [TPS Nov 2021] Synthetic spam emails data	Identify spam emails via features extracted from the email
AutoML Binary Classification (Random Forest, SVR, Blending) using PyCaret with Intel® Extension for Scikit-learn* Data: [TPS Jan 2022] Fictional Sales data	Predict the corresponding item sales for each date-country-store-item combination