32 datasets found

The PennML benchmark suite consists of over 90 regression problems and provides a performance overview of several common regression algorithms.

The dataset used in the paper for hyper-parameter tuning using transient cloud resources.

The dataset used in the paper for hyper-parameter tuning using transient cloud resources.

The dataset used in the paper is a SARCOS regression dataset, which contains 44,484 training samples and 4,449 testing samples.

The dataset used in the paper is a regression dataset with exchangeable data.

The ImbR data set consists of regression data set with 1000 cases and two features, X1 (numeric) and X2 (numeric). The target variable, denoted as Tgt, has a continuous...

The dataset used in the paper is a real-world dataset for housing prices.

The dataset used in the paper is a synthetic dataset for regression tasks with corrupted data and heavy-tailed noise.

The shootout dataset is a regression dataset containing information about the reflectance spectra of pharmaceutical tablets.

The auto-mpg dataset is a regression dataset containing information about the fuel efficiency of various car models.

The Ames housing data contains information about houses in the Ames, Iowa neighborhood, including the number of bedrooms, square footage, and other features.

The 1-dimensional toy dataset is a small dataset of regression tasks, each task represented by a single value.

Fisher's Iris dataset is a multivariate dataset introduced by Sir Ronald Fisher in his 1936 paper "The use of multiple measurements in taxonomic problems". It contains 150...

Crash risk prediction model based on Bayesian logistic regression.

The dataset used for regression settings, including Ozone, Machine, MPG, Boston, Student, and Abalone.

The Airbnb dataset is a large dataset containing information about apartments for rent in Chicago. The dataset includes features such as neighborhood, number of beds, amenities,...

The dataset used in the paper is a real dataset from the UCI Machine Learning Repository.

The dataset used in the paper is a real dataset from the UCI Machine Learning Repository.

The dataset used in the paper is a real dataset from the UCI Machine Learning Repository.

The Diabetes dataset contains 10 variables-dimensions for a sample size (number of points) of 442 and a target (label) variable which quantifies diabetes progress.