```
tags: [AI/Tasks/Regression, AI/Algorithms/DecisionTrees ]
aliases: [Decision Tree Regression, DTR]
```

Decision Tree Regression (DTR) is a Machine Learning algorithm used for Regression Tasks predicting continuous numeric values.

DTR algorithm creates a tree-like model where each node represents a feature, and branches represent the feature's value, ultimately leading to a predicted target value at the leaf nodes. By recursively partitioning the input space, DTR effectively builds a predictive model that can capture non-linear relationships within the data.

Notes:

- Unlike Decision Trees Tree classifiers that are used for Classification problems, DTR is designed to predict continuous numeric values rather than discrete class labels as output.
- DTR selects the best Feature and value for splitting the data at each node to minimize the Variance within each subset.
- The algorithm is prone to Overfitting, so techniques like tree pruning and limiting the maximum depth of the tree are often employed.
- DTR is interpretable and can handle both numerical and Categorical Variables, but its interpretability comes at the cost of potential complexity in creating a model that generalizes well to new data.
- DTR is useful Predictive Analysis, especially in scenarios requiring transparent models that allow human insight into the decision-making process.

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