R package to interface with OpenML

A rust interface to http://openml.org/

Implementations for Feedzai's OpenML APIs to allow for usage of machine learning models in the R programming language.

Comparison of Logistic Regression and Random Forest performances on OpenML Datasets

RapidMiner plugin

Integrating OpenML with Linked Data.

No description available

:exclamation: This is a read-only mirror of the CRAN R package repository. OpenML — Open Machine Learning and Open Data Platform. Homepage: https://github.com/openml/openml-r Report bugs for this package: https://github.com/openml/openml-r/issues

For integration C++ API

OpenML Regression Bot Website

Paper for the description of the OpenML R-Bot

An implementation of the OpenMLS storage traits using Redb embedded database

R implementation of an enhanced Random Forest with adaptive sampling for imbalanced classification.

No description available

OPENML Deep Reinforcement Learning

No description available

No description available

Reproduce openml experiments on REANA

No description available

No description available

OpenML CC18 reduced

No description available

1599 instances - 12 features - 6 classes - 0 missing values

Bug: 306906961

Bug: 306902928

No description available

K-MeansBA: A clustering algorithm with size constraints optimized using the Bat Algorithm, implemented in R and evaluated on 100+ OpenML datasets.

:exclamation: This is a read-only mirror of the CRAN R package repository. mlr3oml — Connector Between 'mlr3' and 'OpenML'. Homepage: https://mlr3oml.mlr-org.com, https://github.com/mlr-org/mlr3oml Report bugs for this package: https://github.com/mlr-org/mlr3oml/issues

#KOODINGAN DATASET : THE MNIST DATABASE OF HANDWRITTEN DIGITS from sklearn.datasets import fetch_openml X, y = fetch_openml('mnist_784', data_home='./dataset/mnist', return_X_y=True) X.shape (70000, 784) import matplotlib.pyplot as plt import matplotib.cm as cm pos = 1 for data in x[:8]: plt.subplot((1, 8, pos) plt.imshow((data.reshape((28, 28)); cmap=cm.Greys_r) plt.axis('off') pos += 1 plt.show() y[:8] # X_train = X[60000:] # X_train = y[60000:] # X_test = X[:60000] # X_test = y[:60000] X_train = x[:1000] y_train = y[:1000] X_test = X[69000:] y_test = y[69000:]