Decision Trees in Data Science

Decision trees (DTs) are among the most versatile class of machine learning algorithm. Decision Trees are a non-parametric supervised learning method used for classification and regression. The goal is to create‧‧‧

Decision trees (DTs) are among the most versatile class of machine learning algorithm. Decision Trees 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. capable of handling both classification and regression tasks and they’re able to deal with complex nonlinear datasets. They’re particularly useful as the basic classifier and random forests which are among the most powerful class of machine learning algorithm. Decision trees are known as white-box models. This means that decision trees are easy to interpret. This is because they’re based on a hierarchy of simple classification rules which are easily visualised. This is an opposition to black-box models like deep neural networks. In black-box models, decisions are made in a process which is far more opaque. With decision trees, we can easily traverse the tree by eye and see the criteria for how the decisions are made.


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