Automatic model tuning is a technique used in machine learning and artificial intelligence to automatically optimise the hyperparameters of a predictive model. Hyperparameters are parameters that are not learned during model training, but are set before training and directly affect model performance.
Auto-tuning of predictive models involves automatically selecting the best values of hyperparameters by systematically exploring different possible combinations and evaluating their performance on a validation set. This technique can be used in a wide variety of machine learning models, such as decision trees, neural networks and support vector machines.
Auto-tuning predictive models can significantly improve the performance and accuracy of a predictive model, especially on large and complex datasets. By automatically optimising hyperparameters, the need for manual tuning and human intervention can be reduced, which can save time and resources and improve the scalability and efficiency of the modelling process.
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