Thus, the algorithm optimizes the surrogate and suggests the hyperparameter values at the maximum of the surrogate model as the optimal values for the original function as well.

What is this book about? Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the ...

The primary goal of a linear regression training algorithm is to compute coefficients that make the difference between reality and the model’s predictions consistently small.

This article presents a methodology for the modeling of high-speed systems using machine learning methods. A multilayer perceptron neural network is used to map the input- output characteristics from ...

Yellowbrick is a suite of visual diagnostic tools called "Visualizers" that extend the scikit-learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines ...

Multi-scale decision system (MDS) is an effective tool to describe hierarchical data in machine learning. Optimal scale combination (OSC) selection and attribute reduction are two key issues related ...

As it currently stands, the vast majority of the AI advancements and applications you hear about refer to a category of algorithms known as machine learning (see "What is machine learning?").