Machine Learning with R, the tidyverse, and mlr
🆕 In this episode: Hefin Rhys, Machine Learning with R, the tidyverse, and mlr.
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Episode Show Notes:
Democratization of machine learning will drive tech innovation because of expanding AI applications. This will enable training of advanced machine-learning algorithms and create innovations in natural language processing, image detection and more.
Machine learning with R, the Tidyverse and mlr offer a unified approach to modelling because of their ease of readability. Defining machine-learning tasks is challenging, and mlr makes it easy for validation, readability and benchmarking by providing the same training set.
Nested cross validation, feature selection and developing computation graphs are becoming easier because of mlr, which creates a unified interface for machine-learning algorithms. Mlr is the answer to Scikit learn and performs functions such as imputation, hyper-parameter tuning and performance metrics. Mlr combination with deep learning models makes it easier for learning and training data sets.