================= What is HoNCAML ================= HoNCAML (Holistic No Code Automated Machine Learning) is a tool aimed to run automated machine learning pipelines, and specifically focused on finding the best model and hyperparameters for the problem at hand. Following the `no code paradigm `_, no Python knowledge is needed. There are two ways to define pipelines: * Through the Graphical User Interface * Through `YAML `_ configuration files HoNCAML (Holistic No Code Automated Machine Learning) is a tool aimed to run automated machine learning pipelines, and specifically focused on finding the best model and hyperparameters for the problem at hand. Pipelines ========= There are three types of provided pipelines. Train ----- Train a specific model with the hyperparameters specified. - Input: A dataset for the training. - Output: The model object stored to disk. Predict ------- Use a model to generate predictions for a specific dataset. - Input: A dataset for the test, together with a model object. - Output: A tabular file with the predictions. Benchmark --------- Search for the best model and hyperparameters for the dataset at hand. - Input: A dataset for the benchmark. - Output: Main output is a configuration file with the best model and hyperparameters, and a tabular file with the results for all configurations tested. Focus ===== HoNCAML has been designed having the following aspects in mind: * Ease of use * Modularity * Extensibility Users ===== HoNCAML does not assume any kind of technical knowledge, but at the same time it is designed to be extended by expert people. Therefore, its user base may range from: * **Basic users**: In terms of programming experience and/or machine learning knowledge. It would be possible for them to get results in an easy way. * **Advanced users**: It is possible to customize experiments in order to adapt to a specific use case that may be needed by an expert person. Support ======= Regarding each of the following concepts, HoNCAML supports specific sets of them; nevertheless, due to its nature, extend the library further should be not only feasible, but intuitive. Data structure -------------- For now only data with tabular format is supported. However, HoNCAML provides special preprocessing methods if needed: * Normalization * One hot encoding of categorical features Problem type ------------ At this moment, the following types of problems are supported: * Regression * Classification Model type ---------- Regarding available models, the following are supported: * Sklearn models (ML) * Pytorch models (DL)