Kaggle Kernels for Classification Tasks

The following Kaggle kernels show how to patch scikit-learn with Intel® Extension for Scikit-learn* for various classification tasks. These kernels usually include a performance comparison between stock scikit-learn and scikit-learn patched with Intel® Extension for Scikit-learn*.

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

Binary Classification

Kernel	Goal	Content
Logistic Regression for Binary Classification Data: [TPS Nov 2021] Synthetic spam emails data	Identify spam emails via features extracted from the email	data preprocessing (normalization) search for optimal parameters using Optuna training and prediction using scikit-learn-intelex performance comparison to scikit-learn
Feature Importance in Random Forest for Binary Classification Data: [TPS Nov 2021] Synthetic spam emails data	Identify spam emails via features extracted from the email	reducing DataFrame memory usage computing feature importance with ELI5 and the default scikit-learn permutation importance training using scikit-learn-intelex performance comparison to scikit-learn
Random Forest for Binary Classification Data: [TPS Apr 2021] Synthetic data based on Titanic dataset	Predict whether a passenger survivies	data preprocessing feature construction search for optimal parameters using Optuna training and prediction using scikit-learn-intelex performance comparison to scikit-learn
Support Vector Classification (SVC) for Binary Classification Data: [TPS Apr 2021] Synthetic data based on Titanic dataset	Predict whether a passenger survivies	data preprocessing training and prediction using scikit-learn-intelex performance comparison to scikit-learn
Support Vector Classification (SVC) with Feature Preprocessing for Binary Classification Data: [TPS Apr 2021] Synthetic data based on Titanic dataset	Predict whether a passenger survivies	data preprocessing feature engineering training and prediction using scikit-learn-intelex performance comparison to scikit-learn

MultiClass Classification

Kernel	Goal	Content
Logistic Regression for MultiClass Classification with Quantile Transformer Data: [TPS Jun 2021] Synthetic eCommerce data	Predict the category of an eCommerce product	data preprocessing with Quantile Transformer training and prediction using scikit-learn-intelex search for optimal paramters using Optuna performance comparison to scikit-learn
Support Vector Classification (SVC) for MultiClass Classification Data: [TPS May 2021] Synthetic eCommerce data	Predict the category of an eCommerce product	data preprocessing training and prediction using scikit-learn-intelex
Stacking Classifer with Logistic Regression, kNN, Random Forest, and Quantile Transformer Data: [TPS Jun 2021] Synthetic eCommerce data	Predict the category of an eCommerce product	data preprocessing: one-hot encoding, dimensionality reduction with PCA, normalization creating a stacking classifier with logistic regression, kNN, and random forest, and a pipeline of Quantile Transformer and another logistic regression as a final estimator searching for optimal parameters for the stacking classifier training and prediction using scikit-learn-intelex performance comparison to scikit-learn
Support Vector Classification (SVC) for MultiClass Classification Data: [TPS Dec 2021] Synthetic Forest Cover Type data	Predict the forest cover type	data preprocessing training and prediction using scikit-learn-intelex performance comparison to scikit-learn
Feature Importance in Random Forest for MultiClass Classification Data: [TPS Dec 2021] Synthetic Forest Cover Type data	Predict the forest cover type	reducing DataFrame memory usage computing feature importance with ELI5 training and prediction using scikit-learn-intelex performance comparison to scikit-learn
k-Nearest Neighbors (kNN) for MultiClass Classification Data: [TPS Feb 2022] Bacteria DNA	Predict bacteria species based on repeated lossy measurements of DNA snippets	data preprocessing training and prediction using scikit-learn-intelex performance comparison to scikit-learn

Classification Tasks in Computer Vision

Kernel	Goal	Content
Support Vector Classification (SVC) for MultiClass Classification (CV task) Data: Digit Recognizer (MNIST)	Recognize hand-written digits	data preprocessing training and prediction using scikit-learn-intelex performance comparison to scikit-learn
k-Nearest Neighbors (kNN) for MultiClass Classification (CV task) Data: Digit Recognizer (MNIST)	Recognize hand-written digits	training and prediction using scikit-learn-intelex performance comparison to scikit-learn

Classification Tasks in Natural Language Processing

Kernel	Goal	Content
Support Vector Classification (SVC) for a Binary Classification (NLP task) Data: Natural Language Processing with Disaster Tweets	Predict which tweets are about real disasters and which ones are not	data preprocessing TF-IDF calculation search for optimal paramters using Optuna training and prediction using scikit-learn-intelex performance comparison to scikit-learn
One-vs-Rest Support Vector Machine (SVM) with Text Data for MultiClass Classification Data: What’s Cooking	Use recipe ingredients to predict the cuisine	feature extraction using TfidfVectorizer training and prediction using scikit-learn-intelex performance comparison to scikit-learn
Support Vector Classification (SVC) for Binary Classification with Sparse Data (NLP task) Data: Stack Overflow questions	Predict the binary quality rating for Stack Overflow questions	data preprocessing TF-IDF calculation search for optimal paramters using Optuna training and prediction using scikit-learn-intelex performance comparison to scikit-learn