What is decision tree in machine learning?
A decision tree is a supervised machine learning tool that may be used to classify or forecast data based on how queries from the past have been answered. The model is supervised learning in nature, which means that it is trained and tested using data sets that contain the required categorisation.
Why is decision tree used in ML?
In machine learning, decision trees are a method for structuring the algorithm. The dataset features will be divided using a decision tree technique and a cost function. Pruning, a method used to remove branches that might use pointless features, is done on the decision tree before it is optimized.
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Which algorithm is used in decision tree?
The ID3 (by Quinlan) method is the fundamental formula used in decision trees. The ID3 algorithm builds decision trees using a top-down, greedy approach.
When decision tree is most commonly used?
Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in machine learning.
What are the advantages and disadvantages of decision trees?
Advantages of Decision Trees:
Interpretability
Less Data Preparation
Non-Parametric
Versatility
Non-Linearity
Disadvantages of Decision Tree:
Overfitting
Feature Reduction & Data Resampling
Optimization
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