
ELLISP . GITHUB . IO {
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Title[redir]:
Moving largish data from R to H2O - spam detection with Enron emails
Description:
I finally solve my problem of writing large sparse matrices from R into SVMLight format for importing to H2O; and demonstrate application with spam detection trained on the Enron email data comparing a generalized linear model, random forest, gradient boosting machine, and deep neural network.
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Keywords {🔍}
spam, data, matrix, neg, ham, sparse, words, format, enron, word, model, enronsplit, text, pos, svmlight, machine, columns, number, true, emails, writing, package, frame, file, validation, files, variables, dense, glm, email, random, learning, dataset, times, rows, column, vector, analysis, models, xnames, detection, linear, forest, gradient, boosting, row, triplet, time, seconds, testing,
Topics {✒️}
file=tl_files/project_datasets/enron-spam%20datasets/preprocessed/enron gradient boosting machine efficiently-transform-sparse-triplet deep neural network php/enron-spam-datasets neural network machine learning purposes blog posts featuring aidan mc nulty simplet triplet matrix elastic net regularization statistical learning methods top rated answer /questions/41477700/optimising-sapply sensational corporate scandals original authors classified efficient computing wise presidential inauguration speeches enron email collection generalized linear model simple triplet matrix peter ellis single validation frame largest published sets simple triple matrix document term matrix document-term-matrix free range statistics means multiple copies medium sized data blessed data scientists sparse matrix object york times articles svmlight format combines enron email dataset functionality recently added minimal baseline sort stops growing trees proudly supporting development writing dense matrices pre-processed data convenient pre-processing nested paste statements traditional data frame stack overflow interaction writing svmlight format york times matrix efficiently write files choosing tuning parameters spam detection trained
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