What are the most common mistakes to avoid when using decision trees in data mining?

1 Choosing the wrong splitting criterion

One of the key decisions when building a decision tree is how to split the data at each node. There are different criteria that can be used, such as entropy, gini index, or information gain. However, not all of them are suitable for every type of problem. For example, entropy and information gain tend to favor splits that create many branches, which can lead to overfitting and complexity. On the other hand, gini index tends to favor splits that create fewer branches, which can lead to underfitting and simplicity. Therefore, you should choose the splitting criterion that matches your objective and data characteristics.

2 Ignoring feature selection and scaling

Another important decision when building a decision tree is which features to use and how to scale them. Some features may be irrelevant, redundant, or noisy, and can reduce the quality and accuracy of the decision tree. Therefore, you should perform feature selection to identify and remove the features that do not contribute to the prediction. Similarly, some features may have different scales and ranges, and can affect the splitting criterion and the tree structure. Therefore, you should perform feature scaling to normalize or standardize the features so that they have comparable values and distributions.

3 Not pruning or cross-validating the tree

A common mistake when using decision trees is to grow the tree until it perfectly fits the training data, without considering the generalization and validation of the model. This can result in overfitting, where the tree captures the noise and outliers of the training data, but fails to perform well on new and unseen data. To avoid this, you should prune the tree by setting a limit on the depth, the number of nodes, or the minimum samples per node. Pruning can help to reduce the complexity and size of the tree, and improve its accuracy and robustness. Moreover, you should cross-validate the tree by splitting the data into training, validation, and test sets, and evaluating the performance of the tree on each set. Cross-validation can help to optimize the parameters and hyperparameters of the tree, and estimate its error and variance.

4 Not visualizing or interpreting the tree

A major advantage of decision trees is that they are easy to visualize and interpret, as they provide a clear and logical representation of the decision process and the rules that govern it. However, some users may neglect or overlook this aspect, and focus only on the numerical outputs and metrics of the tree. This can lead to missing some valuable insights and explanations that the tree can offer, such as the importance and interaction of the features, the distribution and heterogeneity of the data, and the trade-offs and uncertainties involved in the prediction. Therefore, you should always visualize and interpret the tree, either by using graphical tools or by extracting the rules and conditions from the tree structure.

5 Not comparing or combining with other methods

A final mistake when using decision trees is to assume that they are the best or the only method for data mining, and to ignore or disregard other methods that may be more suitable or complementary. Decision trees have some limitations and drawbacks, such as their sensitivity to data changes, their instability and variability, and their tendency to create biased or imbalanced trees. Therefore, you should always compare and contrast the decision tree with other methods, such as regression, classification, clustering, or association. Moreover, you should consider combining the decision tree with other methods, such as ensemble learning, boosting, or bagging. These methods can help to improve the performance and diversity of the decision tree, and to overcome some of its challenges and weaknesses.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?