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A dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim ...
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
Dask is an open-source Python library for parallel computing.Dask [1] scales Python code from multi-core local machines to large distributed clusters in the cloud. Dask provides a familiar user interface by mirroring the APIs of other libraries in the PyData ecosystem including: Pandas, scikit-learn and NumPy.
The iris data set is widely used as a beginner's dataset for machine learning purposes. The dataset is included in R base and Python in the machine learning library scikit-learn, so that users can access it without having to find a source for it. Several versions of the dataset have been published. [8]
Expression tree as it can be used in symbolic regression to represent a function. Symbolic regression (SR) is a type of regression analysis that searches the space of mathematical expressions to find the model that best fits a given dataset, both in terms of accuracy and simplicity.
The above example demonstrate the simplicity behind the API design, which makes it similar to popular Python based machine learning kit (scikit-learn). Our objective is to simplify for the user the API and the main machine learning functions such as Classify and Predict.
scikit-learn includes a Python implementation of DBSCAN for arbitrary Minkowski metrics, which can be accelerated using k-d trees and ball trees but which uses worst-case quadratic memory. A contribution to scikit-learn provides an implementation of the HDBSCAN* algorithm.
scikit-learn, a popular machine learning library in Python implements t-SNE with both exact solutions and the Barnes-Hut approximation. Tensorboard, the visualization kit associated with TensorFlow, also implements t-SNE (online version) The Julia package TSne implements t-SNE