Search results
Results from the WOW.Com Content Network
Amazon reviews US product reviews from Amazon.com. None. 233.1 million Text Classification, sentiment analysis 2015 (2018) [6] [7] McAuley et al. OpinRank Review Dataset Reviews of cars and hotels from Edmunds.com and TripAdvisor respectively. None. 42,230 / ~259,000 respectively Text Sentiment analysis, clustering 2011 [8] [9] K. Ganesan et al ...
Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC.Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
Amazon uses affinity analysis for cross-selling when it recommends products to people based on their purchase history and the purchase history of other people who bought the same item. Family Dollar plans to use market basket analysis to help maintain sales growth while moving towards stocking more low- margin consumable goods .
Data Commons is an open-source platform [1] created by Google [2] that provides an open knowledge graph, combining economic, scientific and other public datasets into a unified view. [3] Ramanathan V. Guha, a creator of web standards including RDF, [4] RSS, and Schema.org, [5] founded the project, [6] which is now led by Prem Ramaswami. [7]
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Hugging Face, Inc. is an American company incorporated under the Delaware General Corporation Law [1] and based in New York City that develops computation tools for building applications using machine learning.
In response to requests from other researchers, GroupLens released three datasets: [32] the MovieLens 100,000 rating dataset, the MovieLens 1 million rating dataset, and the MovieLens 10 million rating dataset. These datasets became the standard datasets for recommender research, and have been used in over 300 papers by researchers around the ...
In 2021, ImageNet-1k was updated by annotating faces appearing in the 997 non-person categories. They found training models on the dataset with these faces blurred caused minimal loss in performance. [31] ImageNetV2 was a new dataset containing three test sets with 10,000 each, constructed by the same methodology as the original ImageNet. [32]