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Hyperparameters Tuning: Grid Search vs Random Search

In this video we talk about two methods that are commonly used to fine-tune the hyperparameters of a statistical model: (1) grid search and (2) random search, and what are the advantages and disadvantages of each one. References ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Grid search vs random search figure taken from: A kernel design approach to improve kernel subspace identification Related Videos ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Covariance and Correlation Explained:    • Covariance and Correlation Explained   Eigendecomposition Explained:    • Eigendecomposition Explained   Multivariate Normal (Gaussian) Distribution Explained:    • Multivariate Normal (Gaussian) Distri...   The Bessel's Correction:    • Why We Divide by N-1 in the Sample Va...   Gradient Boosting with Regression Trees Explained:    • Gradient Boosting with Regression Tre...   P-Values Explained:    • P-Values Explained | P Value Hypothes...   Kabsch-Umeyama Algorithm:    • Kabsch-Umeyama Algorithm - How to Ali...   Contents ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 00:00 - Hyperparameters Intro 01:04 - Grid Search 01:34 - Random Search 02:08 - Pros and cons 02:45 - Outro Follow Me ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 🐦 Twitter: @datamlistic   / datamlistic   📸 Instagram: @datamlistic   / datamlistic   📱 TikTok: @datamlistic   / datamlistic   Channel Support ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ The best way to support the channel is to share the content. ;) If you'd like to also support the channel financially, donating the price of a coffee is always warmly welcomed! (completely optional and voluntary) ► Patreon:   / datamlistic   ► Bitcoin (BTC): 3C6Pkzyb5CjAUYrJxmpCaaNPVRgRVxxyTq ► Ethereum (ETH): 0x9Ac4eB94386C3e02b96599C05B7a8C71773c9281 ► Cardano (ADA): addr1v95rfxlslfzkvd8sr3exkh7st4qmgj4ywf5zcaxgqgdyunsj5juw5 ► Tether (USDT): 0xeC261d9b2EE4B6997a6a424067af165BAA4afE1a #hyperparameters #finetune #gridsearch #randomsearch