Around 10% of almost any predictive modeling project
is spent in predictive modeling, 'funModeling' and the book <http://
livebook.datascienceheroes.com/> are intended to cover remaining 90%: data
preparation, profiling, selecting best variables 'dataViz', assessing model
performance and other functions.

Learn Data Science Through the "Data Science Live Book"

ROCR, ggplot2, gridExtra, pander, reshape2, scales, dplyr,
lazyeval, utils, RColorBrewer

Exploratory Data Analysis and Data Preparation Tool-Box Book
Around 10% of almost any predictive modeling project is spent in predictive modeling, 'funModeling' and the book Data Science Live Book (<http://livebook.datascienceheroes.com/>) are intended to cover remaining 90%: data preparation, profiling, selecting best variables 'dataViz', assessing model performance and other functions.
ROCR, ggplot2, gridExtra, pander, reshape2, scales, dplyr,
lazyeval, utils, RColorBrewer, moments

ROCR, ggplot2, gridExtra, pander, reshape2, scales, dplyr,
lazyeval, utils, RColorBrewer, moments, entropy

person("Pablo", "Casas", role = c("aut", "cre"), email = "pcasas.biz@gmail.com")

Around 10% of almost any predictive modeling project is spent in predictive modeling, 'funModeling' and the book Data Science Live Book (<https://livebook.datascienceheroes.com/>) are intended to cover remaining 90%: data preparation, profiling, selecting best variables 'dataViz', assessing model performance and other functions.
ROCR, ggplot2, gridExtra, pander, reshape2, scales, dplyr,
lazyeval, utils, RColorBrewer, moments, entropy, pROC

Exploratory Data Analysis and Data Preparation Tool-Box
ROCR, ggplot2, gridExtra, pander, reshape2, scales, dplyr,
lazyeval, utils, RColorBrewer, moments, entropy, cli, stringr

Fungal Species Identification using DNA Barcode with Random
Forest
A machine learning based approach for fungal species identification using barcode sequence data. The multi-class random forest model has been used for prediction purpose, where the gap-pair compositional feature was used to encode the barcode sequence data. The encoded dataset was used as input for prediction purpose. Though this approach has been developed for fungal species identification in particular, can be used for other species identification as well.