Decision Trees - Hyperparameters | Overfitting and Underfitting in Decision Trees

In Decision Trees, hyperparameters play a crucial role in managing model complexity. Common hyperparameters include 'max_depth' to control the tree's depth, 'min_samples_split' for the minimum samples required to split a node, and 'min_samples_leaf' for the minimum samples in a leaf node. Balancing these hyperparameters is vital to avoid overfitting (capturing noise) or underfitting (missing patterns) in the model. Visualization Tool used: https://dt-visualise.herokuapp.com/ ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in/s/store ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at [email protected] ⌚Time Stamps⌚ 00:00 - Intro 01:19 - Depth of tree 04:26 - Geometrical Intuition of over fitting 07:19 - Geometric Intuition of under fitting 11:46 - Decision tree Hyper parameter tuning 27:05 - Outro