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What is Machine Learning Project Use Case Diagram about?

The document discusses splitting data into test and train sets, testing models on the holdout test set, and reporting results and selected models. Data from multiple sources is cleaned and prepared for training machine learning models. The data is resampled randomly based on source frequency. Part-of-speech and term frequency-inverse document frequency (TFIDF) features are generated from the articles. Several models, including logistic regression, naive bayes, and support vector machines, are trained on the

Author

Sampada Suwal

Language

EN