Decision trees, a popular machine learning algorithm, form tree-like models for classification or regression. Nodes represent decisions based on feature values, and leaves signify final outcomes. Training involves recursive dataset splits to create rules for homogeneous subsets. Common splitting criteria are Gini impurity and mean squared error. Pruning mitigates overfitting. Decision trees are interpretable and adept at handling diverse data types. Despite simplicity, they may not capture complex relationships and are sensitive to noise.
- Advantages:
- Easy to understand and interpret, making them suitable for visual representation.
- Require minimal data preparation.
- Can handle both numerical and categorical data.
- Disadvantages:
- Prone to overfitting, especially with deep trees.
- May not capture complex relationships in the data as effectively as other algorithms.
- Can be sensitive to small variations in the training data.