[Feature Request]: Bayesian Optimization model in Machine Learning

[pavitraag]

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Feature Description

Bayesian Optimization is a sequential model-based optimization technique that uses Bayesian inference to direct the search for the optimal set of hyperparameters in machine learning models. Unlike grid search or random search, Bayesian Optimization builds a probabilistic model of the objective function and uses it to select the most promising hyperparameters to evaluate, thereby optimizing performance with fewer evaluations.

Use Case

In a real-time use case, a data scientist working on a time-sensitive project like crude oil price forecasting can use Bayesian Optimization to quickly identify the best hyperparameters for their predictive model. This leads to more accurate predictions with reduced computational resources, enabling timely and informed decision-making in the energy sector.

Benefits

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Priority

High

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[github-actions]

Hi @pavitraag! Thanks for opening this issue. We appreciate your contribution to this open-source project. Your input is valuable and we aim to respond or assign your issue as soon as possible. Thanks again!

[github-actions]

Hello @pavitraag! Your issue #3616 has been closed. Thank you for your contribution!