Decision Tree is a Supervised learning technique that may be used for both classification and regression issues, but it is most commonly employed for classification.
Decision Trees for Classification Image Result in Machine Learning Share Introduction Decision Trees are a sort of Supervised Machine Learning (you describe what the input is and what the related output is in the training data) in which the data is continually separated based on a specific parameter.
In the shape of a tree structure, decision tree constructs classification or regression models. It incrementally divides a dataset into smaller and smaller sections while also developing an associated decision tree. The end result is a tree containing leaf nodes and decision nodes.
The purpose of employing a Choice Tree is to develop a training model that can predict the class or value of the target variable by learning basic decision rules learned from prior data (training data).
Decision Trees (DTs) are a type of non-parametric supervised learning method that can be used for classification and regression. The goal is to build a model that predicts the value of a target variable using basic decision rules derived from data attributes.
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