Disadvantages of Tree-Based Algorithms
Some of the disadvantages of Tree-Based algorithms are discussed below:
- Overfitting issue: Sometimes tree-based algorithms can get a little too lucky to learn from the data they see. This is called overfitting, where too much attention is paid to the details of training and struggles in the face of unseen new data.
- Sensitivity to small changes: A tree-based algorithm can seem a little mechanically simple. Sometimes small changes in the data can lead to large changes in the model, making it skewed. This sensitivity suggests that it may not be the best choice where the data is noisy or constantly changing.
- High complexity: While decision trees are great at simplifying decision making, when grouped together like Random Forests or Gradient Boosting, things can get a little more complicated. These large groups of trees can be difficult to maintain and understand, making them less straightforward in an implementation.
Tree Based Machine Learning Algorithms
Tree-based algorithms are a fundamental component of machine learning, offering intuitive decision-making processes akin to human reasoning. These algorithms construct decision trees, where each branch represents a decision based on features, ultimately leading to a prediction or classification. By recursively partitioning the feature space, tree-based algorithms provide transparent and interpretable models, making them widely utilized in various applications. In this article, we going to learn the fundamentals of tree based algorithms.
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