Introduction to AutoML
Anyone who has worked in the domain of ML can tell you that building ML models is a complex and iterative process. You start with a dataset and a set of features, and then train a model on that data. As you get more data, you add more features, and you retrain your model. This process continues until you have a model that generalizes well to new data. The task is complicated by the fact that there is a multitude of hyperparameters and that they have a kind of non-linear relationship to model performance. Choosing the right model and selecting the optimum hyperparameters is still considered alchemy by many.
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You can refer to Has artificial intelligence become alchemy? Matthew Hutson, Science, Vol 360, Issue 6388 for more information.
Whether AI is alchemy or not is a hot debate. While many who start experimenting with AI feel that it is alchemy, there are experts, including us authors, who believe it is not so. AI, like any other experimental science...