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Which of the following is a advantage of decision trees?
A significant advantage of a decision tree is that it forces the consideration of all possible outcomes of a decision and traces each path to a conclusion. It creates a comprehensive analysis of the consequences along each branch and identifies decision nodes that need further analysis.
What are the strengths of using decision trees?
Advantages of Decision Trees
- Easy to read and interpret. One of the advantages of decision trees is that their outputs are easy to read and interpret without requiring statistical knowledge.
- Easy to prepare.
- Less data cleaning required.
Is the problem of learning a decision tree NP-complete?
The problem of learning an optimal decision tree is known to be NP-complete under several aspects of optimality and even for simple concepts. Consequently, practical decision-tree learning algorithms are based on heuristic algorithms such as the greedy algorithm where locally optimal decisions are made at each node.
How are decision trees used in supervised learning?
Decision Trees ¶ Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
How are decision trees used in classification and regression?
Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
What are the disadvantages of a decision tree?
The disadvantages of decision trees include: Decision-tree learners can create over-complex trees that do not generalise the data well. This is called overfitting. Decision trees can be unstable because small variations in the data might result in a completely different tree being generated.