The document is a test for English language proficiency. It contains questions about language use and writing ability. The language use section includes questions about oral expressions, situational dialogs, and error identification involving grammar, vocabulary and punctuation. The writing ability section focuses on writing at the sentence and paragraph level, with questions about completing sentences and paragraphs.
The document discusses using xgboost for machine learning and summarizes steps to prepare data for xgboost models. It recommends binding feature data together and writing it out in the libsvm format for efficient reading into an xgboost DMatrix object. It also suggests using the data.table package to write out libsvm files in parallel for improved performance on large datasets.
The document is a test for English language proficiency. It contains questions about language use and writing ability. The language use section includes questions about oral expressions, situational dialogs, and error identification involving grammar, vocabulary and punctuation. The writing ability section focuses on writing at the sentence and paragraph level, with questions about completing sentences and paragraphs.
The document discusses using xgboost for machine learning and summarizes steps to prepare data for xgboost models. It recommends binding feature data together and writing it out in the libsvm format for efficient reading into an xgboost DMatrix object. It also suggests using the data.table package to write out libsvm files in parallel for improved performance on large datasets.