CatBoost Classifier | Machine Learning | Python
Categorical Boosting (𝐂𝐚𝐭𝐁𝐨𝐨𝐬𝐭) is a gradient-boosting algorithm for machine learning. Gradient boosting is a process in which many decision trees are constructed iteratively. In CatBoost, each successive tree is built with reduced loss compared to the previous trees.
I used 𝐦𝐮𝐬𝐡𝐫𝐨𝐨𝐦𝐬.𝐜𝐬𝐯 dataset for this example. The dataset is available in the repository. It contains 2 types of mushrooms in the target column: 𝐞𝐝𝐢𝐛𝐥𝐞 & 𝐩𝐨𝐢𝐬𝐨𝐧𝐨𝐮𝐬.
𝗚𝗶𝘁𝗛𝘂𝗯 𝗮𝗱𝗱𝗿𝗲𝘀𝘀: https://github.com/randomaccess2023/M...
𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝘁𝗶𝗺𝗲𝘀𝘁𝗮𝗺𝗽𝘀:
00:58 - Import required libraries
03:05 - Load 𝐦𝐮𝐬𝐡𝐫𝐨𝐨𝐦𝐬 dataset
06:31 - Perform preprocessing
08:28 - Separate features and classes
09:28 - Split the dataset
10:49 - Apply 𝐂𝐚𝐭𝐁𝐨𝐨𝐬𝐭 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫
16:08 - Plot 𝐜𝐨𝐧𝐟𝐮𝐬𝐢𝐨𝐧_𝐦𝐚𝐭𝐫𝐢𝐱
21:18 - Print 𝐜𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧_𝐫𝐞𝐩𝐨𝐫𝐭
21:53 - Feature importance
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