RandomizedSearchCV using Scikit-Learn

𝐑𝐚𝐧𝐝𝐨𝐦𝐢𝐳𝐞𝐝𝐒𝐞𝐚𝐫𝐜𝐡𝐂𝐕 is a hyperparameter tuning technique that comes with the Scikit-Learn library. It explores a predefined search space of hyperparameters and randomly selects a few combinations to evaluate model performance. Unlike GridSearchCV which systematically examines all the possible combinations, RandomizedSearchCV selects a fixed number of combinations randomly. If the hyperparameter search space is very large, RandomizedSearchCV tends to become a more efficient method for the purpose of hyperparameter tuning. On the other hand, GridSearchCV is a more suitable option if the search space is relatively small and computationally feasible. 𝑮𝒊𝒕𝑯𝒖𝒃 𝒂𝒅𝒅𝒓𝒆𝒔𝒔: https://github.com/randomaccess2023/MG2023/tree/main/Video%2082 𝙄𝙢𝙥𝙤𝙧𝙩𝙖𝙣𝙩 𝙩𝙞𝙢𝙚𝙨𝙩𝙖𝙢𝙥𝙨: 01:25 - Import required libraries 03:06 - Load 𝗶𝗻𝗱𝗶𝗮𝗻_𝗹𝗶𝘃𝗲𝗿_𝗽𝗮𝘁𝗶𝗲𝗻𝘁_𝗱𝗮𝘁𝗮𝘀𝗲𝘁 05:45 - Drop null values 06:32 - Perform preprocessing 08:22 - Separate features and classes 09:09 - Apply 𝗥𝗮𝗻𝗱𝗼𝗺𝗶𝘇𝗲𝗱𝗦𝗲𝗮𝗿𝗰𝗵𝗖𝗩 in 𝗥𝗮𝗻𝗱𝗼𝗺 𝗙𝗼𝗿𝗲𝘀𝘁 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗲𝗿 16:24 - Apply 𝗥𝗮𝗻𝗱𝗼𝗺𝗶𝘇𝗲𝗱𝗦𝗲𝗮𝗿𝗰𝗵𝗖𝗩 in 𝗞 𝗡𝗲𝗶𝗴𝗵𝗯𝗼𝗿𝘀 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗲𝗿 22:05 - Apply 𝗥𝗮𝗻𝗱𝗼𝗺𝗶𝘇𝗲𝗱𝗦𝗲𝗮𝗿𝗰𝗵𝗖𝗩 in 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗿𝗲𝗲 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗲𝗿 25:06 - Apply 𝗥𝗮𝗻𝗱𝗼𝗺𝗶𝘇𝗲𝗱𝗦𝗲𝗮𝗿𝗰𝗵𝗖𝗩 in all the models #sklearn #scikitlearn #randomizedsearchcv #jupyternotebook #python #pythonprogramming #indianliverpatientdataset #datascience #machinelearning #hyperparametertuning