Kaggle Kernels for Regression Tasks

The following Kaggle kernels show how to patch scikit-learn with Intel® Extension for Scikit-learn* for various regression 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.

Using a Single Regressor

Kernel	Goal	Content
Baseline Nu Support Vector Regression (nuSVR) with RBF Kernel Data: [TPS Jul 2021] Synthetic pollution data	Predict air pollution measurements over time based on weather and input values from multiple sensors	data preprocessing search for optimal paramters using Optuna training and prediction using scikit-learn-intelex
Nu Support Vector Regression (nuSVR) Data: [TPS Aug 2021] Synthetic loan data	Calculate loss associated with a loan defaults	data preprocessing feature engineering training and prediction using scikit-learn-intelex performance comparison to scikit-learn
Nu Support Vector Regression (nuSVR) Data: House Prices dataset	Predict sale prices for a property based on its characteristics	data preprocessing exploring outliers feature engineering filling missing values search for optimal parameters using Optuna training and prediction using scikit-learn-intelex performance comparison to scikit-learn
Random Forest Regression Data: [TPS Jul 2021] Synthetic pollution data	Predict air pollution measurements over time based on weather and input values from multiple sensors	checking correlation between features search for best paramters using GridSearchCV training and prediction using scikit-learn-intelex performance comparison to scikit-learn
Random Forest Regression with Feature Engineering Data: [TPS Jul 2021] Synthetic pollution data	Predict air pollution measurements over time based on weather and input values from multiple sensors	data preprocessing feature engineering search for optimal parameters using Optuna training and prediction using scikit-learn-intelex performance comparison to scikit-learn
Random Forest Regression with Feature Importance Computation Data: [TPS Mar 2022] Spatio-temporal traffic data	Forecast twelve-hours of traffic flow in a major U.S. metropolitan area	feature engineering computing feature importance with ELI5 training and prediction using scikit-learn-intelex performance comparison to scikit-learn
Ridge Regression Data: [TPS Sep 2021] Synthetic insurance data	Predict the probability of a customer making a claim upon an insurance policy	data preprocessing filling missing values search for optimal parameters using Optuna training and prediction using scikit-learn-intelex performance comparison to scikit-learn

Stacking Regressors

Kernel

Goal

Content

Stacking Regressor with Random Fores, SVR, and LASSO

Data: [TPS Jul 2021] Synthetic pollution data

Predict air pollution measurements over time based on weather and input values from multiple sensors

feature engineering
creating a stacking regressor
search for optimal parameters using Optuna
training and prediction using scikit-learn-intelex
performance comparison to scikit-learn

Stacking Regressor with ElasticNet, LASSO, and Ridge Regression for Time-series data

Data: Predict Future Sales dataset

Predict total sales for every product and store in the next month based on daily sales data

data preprocessing
creating a stacking regressor
search for optimal parameters using Optuna
training and prediction using scikit-learn-intelex
performance comparison to scikit-learn