Decision Tree
A decision tree is a powerful and intuitive tool for decision-making and predictive analysis, used in both classification and regression tasks. Its tree-like st...
Decision Trees are intuitive, tree-structured algorithms for classification and regression, widely used for making predictions and decisions in AI.
A Decision Tree is a supervised learning algorithm used for making decisions or predictions based on input data. It is visualized as a tree-like structure where each internal node represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label or a continuous value.
A Decision Tree starts with a root node that splits into branches based on the values of an attribute. These branches lead to internal nodes, which further split until they reach the leaf nodes. The paths from the root to the leaf nodes represent decision rules.
The process of building a Decision Tree involves several steps:
Decision Trees are highly versatile and can be applied in various fields, including:
A Decision Tree is a supervised learning algorithm that uses a tree-like model of decisions and their possible consequences. Each internal node is a test on an attribute, each branch is the result of the test, and each leaf node represents a decision or prediction.
Decision Trees are easy to understand and interpret, versatile for both classification and regression, non-parametric, and can handle both numerical and categorical data.
Decision Trees can overfit the training data, be unstable with small data changes, and may be biased towards attributes with more levels.
Decision Trees are used in healthcare for diagnosis, finance for credit scoring, marketing for customer segmentation, and manufacturing for quality control, among other applications.
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A decision tree is a powerful and intuitive tool for decision-making and predictive analysis, used in both classification and regression tasks. Its tree-like st...
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