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TEIn this video, I explain how the Decision Tree algorithm works and how it can be implemented using Python.
A Decision Tree is a supervised machine learning algorithm used for both classification and regression.
It works by splitting the dataset into smaller subsets based on feature conditions, forming a tree-like structure of decisions.
Each internal node represents a decision based on a feature.
Each branch represents the outcome of that decision.
Each leaf node represents the final prediction.
The model learns by selecting splits that reduce impurity in the data, commonly using metrics such as Gini Index or Entropy.
Key ideas behind Decision Trees:
• Feature-based data splitting
• Measuring impurity in data
• Recursive partitioning of the dataset
• Interpretable model structure
Decision Trees are simple to understand, easy to visualize, and form the foundation for many powerful ensemble models like Random Forest.
[decision tree algorithm, machine learning basics, classification model, regression model, scikit learn python, supervised learning, data science, ml algorithms]
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