Decision Tree Disadvantages
Decision trees are popular and powerful algorithms in machine learning, known for their ability to handle complex classification and regression tasks. However, like any other algorithm, decision trees come with their own set of limitations and disadvantages. In this blog post, we will explore the drawbacks of decision trees, providing insights into the considerations you need to keep in mind when working with this algorithm. Overfitting: One of the primary disadvantages of decision trees is their tendency to overfit the training data. Decision trees have a high capacity to learn intricate details and patterns in the training set, which can lead to poor generalization and performance on unseen data. Overfitting occurs when a tree becomes too complex and captures noise or outliers in the training data, compromising its ability to make accurate predictions on new instances. Lack of Robustness: Decision trees are highly sensitive to small changes in the training data. Even slight variation