74 datasets found

The London house prices dataset contains details for property sales and contains around 1.38 million observations.

Adult Income Census dataset

LEURN: a neural network architecture that learns univariate decision rules

The Boston dataset is a well-known, small dataset (506 in-stances) available from the StatLib repository that is popular for evaluating regression models. It contains...

The dataset used in this paper is a synthetic dataset for regression problems, specifically for learning the conditional mean, median, and mode functions.

Standard data sets from UCI repository or Kaggle, including California Housing, Boston, Abalone, Auto, Red Wine, Concrete, Fish Toxicity, NBA salary, and others.

Simulated data from five standard data generating processes (DGPs) to evaluate when SGTs and Booging outperform their counterparts relying on their traditionally associated...

Student performance dataset is a regression task which contains 33 students as training and testing examples and their performance based on features such as average number of...

Boston house prices dataset is a classical linear regression task which contains 506 houses as training and testing examples and their prices based on features such as weighted...

Synthetic regression problem with two users attempting to fit a linear model of the function f (x1, x2) = 5x1 − 2x2 + 0.5x3^2. Each user has input data drawn from a distinct...

The dataset used in this paper for estimating predictive uncertainty in satellite precipitation interpolation with ensemble learning.

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 Zillow.

The dataset used in the paper is AutoML-3, OnlineNewsPop, MNIST-0, MNIST-4, and Zillow.

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 this paper is the Brightdata dataset.

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

The dataset used in the paper is a motorcycle dataset, which is used to evaluate the proposed algorithm.