Hyperparameters in Decision Trees
Decision trees are versatile algorithms used in machine learning that perform classification and regression tasks. They can even handle multi-output tasks for various predictive modeling tasks. A model parameter is an adjustable parameter that is said to be learned from the training data during the model’s training process. In decision trees, there are two types of model parameters such as learnable and non-learnable parameters.
- Learnable parameters: Learnable parameters are calculated or updated iteratively during the training phase of the model. They play a potential role in capturing the patterns and relationships present in the training data. Most importantly, the model gains the ability to learn the optimal values for these parameters autonomously, without requiring external assistance.
- Hyperparameter: Non-learnable parameters are also called hyperparameters. A hyperparameter is a parameter that is defined before the learning process begins and it helps to control aspects of the learning process. Examples of hyperparameters include the learning rate, regularization strength, and the choice of optimization algorithm. When we define these hyperparameters, the model can control the features of the learning process and possibly impact its performance and behavior.
How to tune a Decision Tree in Hyperparameter tuning
Decision trees are powerful models extensively used in machine learning for classification and regression tasks. The structure of decision trees resembles the flowchart of decisions helps us to interpret and explain easily. However, the performance of decision trees highly relies on the hyperparameters, selecting the optimal hyperparameter can significantly impact the model’s accuracy, generalization ability, and robustness.
In this article, we will explore the different ways to tune the hyperparameters and their optimization techniques with the help of decision trees.
Table of Content
- Hyperparameters in Decision Trees
- Why Tune hyperparameters in Decision Trees?
- Methods for Hyperparameter Tuning in Decision Tree
- Implementing Hyperparameter Tuning in a decision Tree
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