Decision Trees - Hyperparameters | Overfitting and Underfitting in Decision Trees
Channel: CampusX
Date: 2021-07-03

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/

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⌚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