ML Parameters Optimization: GridSearch, Bayesian, Random

Offered By
In this Guided Project, you will:

Understand the difference between hyperparameters optimization techniques such as GridSearch, Bayesian & Random Search Optimization Techniques.

Optimize ML model hyperparameters in Scikit-Learn using GridSearch, Bayesian & Random Search Optimization Techniques.

Evaluate several trained regression models performance using various Key Performance Indicators (KPIs).

2 hours
Beginner
No download needed
Split-screen video
English
Desktop only

Hello everyone and welcome to this new hands-on project on Machine Learning hyperparameters optimization. In this project, we will optimize machine learning regression models parameters using several techniques such as grid search, random search and Bayesian optimization. Hyperparameter optimization is a key step in developing machine learning models and it works by fine tuning ML models so they can optimally perform on a given dataset.

Skills you will develop

  • Data Analysis

  • Machine Learning

  • Mathematical Optimization

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Understand the Problem Statement

  2. Import Libraries and Datasets

  3. Practice Opportunity #1 [Optional]:

  4. Practice Opportunity #2 [Optional]

  5. Train an XG-Boost Algorithm Without Optimization

  6. Practice Opportunity #3 [Optional]

  7. Strategy #1: Optimization Using GridSearch

  8. Strategy #2: Optimization Using Random Search

  9. Strategy #3: Optimization Using Bayesian Optimizer

  10. Final Capstone Project

  11. Final Capstone Project Solution

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In a split-screen video, your instructor guides you step-by-step

Instructor

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