Machine Learning with Julia
summary
Claims three years of exclusive Julia use and practical work on random forests and data curation, in particular time series data. Shows reading/writing CSV, parsing dates, slicing a 24,000×1,000-ish table and filtering ~9,000 rows with probability > 0.17. Uses Julia packages for decision trees that expose and let users change the split/score function, and points to streaming / online-learning packages for incremental updates rather than full retraining. Performance and concise package code are emphasized.
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chapters (4)
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