AutoML
Automated machine learning AutoML is a relatively new field in machine learning. It mainly automates all time-consuming processes in machine learning, such as data preprocessing, optimal algorithm selection, hyperparameter tuning, etc., which can save a lot of time in building machines. in the process of learning the model.
Automatic Machine Learning AutoML: For, let denote the feature vector, denote the corresponding target value. Given a training dataset and feature vectors from a test dataset drawn from the same data distribution as it, along with a resource budget and a loss metric, the AutoML problem is to automatically generate predictions for the test set, while the solution to the AutoML problem loss value.
AutoML, is the process of discovering the best performing pipeline for data transformations, models, and model configurations for a dataset.
AutoML typically involves the use of sophisticated optimization algorithms (such as Bayesian optimization) to efficiently navigate the space of possible models and model configurations, and quickly discover the most effective for a given predictive modeling task. It allows non-expert machine learning practitioners to quickly and easily discover