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R does not define a standardized interface for its machine-learning algorithms. Therefore, for any non-trivial experiments, you need to write lengthy, tedious, and error-prone wrappers to call the different algorithms and unify their respective output. {mlr} provides this infrastructure so that you can focus on your experiments! The framework provides supervised methods like classification, regression, and survival analysis along with their corresponding evaluation and optimization methods,...

此專案的目的是要建立簡單、易用、參數說明完整且富有調整性的PHP元件庫，讓網頁程式設計開發者可以輕鬆地建立客製化的網站。 套用當代的術語而言，就是要作為LOW CODE平台的函式庫。